From c95119673985d4d7f5bd6d6f53128c704e802871 Mon Sep 17 00:00:00 2001 From: Andre Lessa Date: Tue, 25 Jul 2023 13:03:35 +0200 Subject: [PATCH] Revert "added file from graphs brnach, was failing to checkout otherwise" This reverts commit a0f9cc069382b032a58a34e24d9d383d25fd04b6. --- Example.ipynb | 896 ------------------------------------------------- parameters.ini | 8 +- 2 files changed, 4 insertions(+), 900 deletions(-) delete mode 100644 Example.ipynb diff --git a/Example.ipynb b/Example.ipynb deleted file mode 100644 index 1025a8dc2..000000000 --- a/Example.ipynb +++ /dev/null @@ -1,896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "e68930e3", - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'runtime' from 'smodels.base' (unknown location)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#!/usr/bin/env python3\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m print_function\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m runtime\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Define your model (list of BSM particles)\u001b[39;00m\n\u001b[1;32m 6\u001b[0m runtime\u001b[38;5;241m.\u001b[39mmodelFile \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmodels.share.models.mssm\u001b[39m\u001b[38;5;124m'\u001b[39m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'runtime' from 'smodels.base' (unknown location)" - ] - } - ], - "source": [ - "#!/usr/bin/env python3\n", - "\n", - "from __future__ import print_function\n", - "from smodels.base import runtime\n", - "# Define your model (list of BSM particles)\n", - "runtime.modelFile = 'smodels.share.models.mssm'\n", - "# runtime.modelFile = 'mssmQNumbers.slha'\n", - "\n", - "from smodels.decomposition import decomposer\n", - "from smodels.base.physicsUnits import fb, GeV, TeV\n", - "from smodels.matching.theoryPrediction import theoryPredictionsFor,TheoryPredictionsCombiner\n", - "from smodels.experiment.databaseObj import Database\n", - "from smodels.tools import coverage\n", - "from smodels.base.smodelsLogging import setLogLevel\n", - "from smodels.share.models.mssm import BSMList\n", - "from smodels.share.models.SMparticles import SMList\n", - "from smodels.base.model import Model\n", - "import time\n", - "import os\n", - "setLogLevel(\"info\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ed387104", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO in databaseObj.loadTextDatabase() in 457: Parsing text database at /Users/sahananarasimha/smodels-database/\n", - "INFO in databaseObj.createBinaryFile() in 608: /Users/sahananarasimha/smodels-database/db3.pcl created.\n", - "INFO in metaObj.printFastlimBanner() in 162: FastLim v1.1 efficiencies loaded. Please cite: arXiv:1402.0492, EPJC74 (2014) 11\n" - ] - } - ], - "source": [ - "# Set the path to the database\n", - "database = Database('official')" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c38e24dd", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'Model' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mModel\u001b[49m(BSMparticles\u001b[38;5;241m=\u001b[39mBSMList, SMparticles\u001b[38;5;241m=\u001b[39mSMList)\n\u001b[1;32m 2\u001b[0m slhafile \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minputFiles/slha/lightEWinos.slha\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39mupdateParticles(inputFile\u001b[38;5;241m=\u001b[39mslhafile)\n", - "\u001b[0;31mNameError\u001b[0m: name 'Model' is not defined" - ] - } - ], - "source": [ - "model = Model(BSMparticles=BSMList, SMparticles=SMList)\n", - "slhafile = 'inputFiles/slha/lightEWinos.slha'\n", - "model.updateParticles(inputFile=slhafile)\n", - "\n", - "# Set main options for decomposition\n", - "sigmacut = 0.5*fb\n", - "mingap = 5.*GeV\n", - "\n", - "t0 = time.time()\n", - "# Decompose model\n", - "topDict = decomposer.decompose(model, sigmacut,\n", - " massCompress=True, invisibleCompress=True,\n", - " minmassgap=mingap)\n", - "\n", - "# Access basic information from decomposition, using the topology list and topology objects:\n", - "print(\"\\n Decomposition done in %1.2fm\" %((time.time()-t0)/60.))\n", - "print(\"\\n Decomposition Results: \")\n", - "print(\"\\t Total number of topologies: %i \" % len(topDict))\n", - "nel = len(topDict.getSMSList())\n", - "print(\"\\t Total number of elements = %i \" % nel)\n", - "# Print information about the m-th topology:\n", - "m = 2\n", - "if len(topDict) > m:\n", - " cName = sorted(topDict.keys())[m]\n", - " elementList = topDict[cName]\n", - " print(\"\\t\\t %i topology = \" % cName)\n", - " # Print information about the n-th element in the m-th topology:\n", - " n = 0\n", - " el = elementList[n]\n", - " print(\"\\t\\t %i-th element = \" % (n), el, end=\"\")\n", - " print(\"\\n\\t\\t\\twith final states =\", el.getFinalStates(), \"\\n\\t\\t\\twith cross section =\", el.weightList, \"\\n\\t\\t\\tand masses = \", el.mass)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "6b0a8565", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(PV > C1+/C1-(1),N2(2)), (C1+/C1-(1) > N1/N1~,q,q), (N2(2) > N1,mu-,mu+)\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "PV\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "C1+/C1-\n", - "\n", - "\n", - "\n", - "0->1\n", - "\n", - 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"metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Loaded Database with 1 UL results and 0 EM results \n", - "\n", - " Theory Predictions and Constraints:\n", - "\n", - " ATLAS-SUSY-2019-08-grp \n", - "------------------------\n", - "Dataset = None\n", - "TxNames = ['TChiWH']\n", - "Theory Prediction = 5.38E-04 [pb]\n", - "Condition Violation = None\n", - "UL for theory prediction = 3.36E+01 [fb]\n", - "r = 1.599E-02\n", - "\n", - "The largest r-value (theory/upper limit ratio) is 1.599E-02\n", - "(The input model is not excluded by the simplified model results)\n", - "\n", - " Theory Predictions done in 0.00m\n" - ] - } - ], - "source": [ - "# Load the experimental results to be used.\n", - "# In this case, all results are employed.\n", - "listOfExpRes = database.getExpResults()\n", - "\n", - "t0 = time.time()\n", - "# Print basic information about the results loaded.\n", - "# Count the number of loaded UL and EM experimental results:\n", - "nUL, nEM = 0, 0\n", - "for exp in listOfExpRes:\n", - " expType = exp.datasets[0].dataInfo.dataType\n", - " if expType == 'upperLimit':\n", - " nUL += 1\n", - " elif expType == 'efficiencyMap':\n", - " nEM += 1\n", - "print(\"\\n Loaded Database with %i UL results and %i EM results \" % (nUL, nEM))\n", - "\n", - "# Compute the theory predictions for each experimental result and print them:\n", - "print(\"\\n Theory Predictions and Constraints:\")\n", - "rmax = 0.\n", - "bestResult = None\n", - "allPredictions = theoryPredictionsFor(database, topDict, combinedResults=False)\n", - "for theoryPrediction in allPredictions:\n", - " print('\\n %s ' % theoryPrediction.analysisId())\n", - " dataset = theoryPrediction.dataset\n", - " datasetID = theoryPrediction.dataId()\n", - " txnames = sorted([str(txname) for txname in theoryPrediction.txnames])\n", - " print(\"------------------------\")\n", - " print(\"Dataset = \", datasetID) # Analysis name\n", - " print(\"TxNames = \", txnames)\n", - " print(\"Theory Prediction = \", theoryPrediction.xsection) # Signal cross section\n", - " print(\"Condition Violation = \", theoryPrediction.conditions) # Condition violation values\n", - "\n", - " # Get the corresponding upper limit:\n", - " print(\"UL for theory prediction = \", theoryPrediction.upperLimit)\n", - "\n", - " # Compute the r-value\n", - " r = theoryPrediction.getRValue()\n", - " print(\"r = %1.3E\" % r)\n", - " # Compute likelihoods for EM-type results:\n", - " if dataset.getType() == 'efficiencyMap':\n", - " theoryPrediction.computeStatistics()\n", - " print('L_BSM, L_SM, L_max = %1.3E, %1.3E, %1.3E' % (theoryPrediction.likelihood(),\n", - " theoryPrediction.lsm(), theoryPrediction.lmax()))\n", - " if r > rmax:\n", - " rmax = r\n", - " bestResult = theoryPrediction.analysisId()\n", - "\n", - " # Print the most constraining experimental result\n", - " print(\"\\nThe largest r-value (theory/upper limit ratio) is %1.3E\" % rmax)\n", - " if rmax > 1.:\n", - " print(\"(The input model is likely excluded by %s)\" % bestResult)\n", - " else:\n", - " print(\"(The input model is not excluded by the simplified model results)\")\n", - "\n", - " print(\"\\n Theory Predictions done in %1.2fm\" %((time.time()-t0)/60.))" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "6ec0ae0d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(PV > C2+(1),N4(2)), (C2+(1) > N1,W+), (N4(2) > N1,higgs)]\n" - ] - } - ], - "source": [ - "tp = allPredictions[0]\n", - "smsList = tp.smsList\n", - "print(smsList)" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "05b73b27", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "PV\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "anyBSM\n", - "\n", - "\n", - "\n", - "0->1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "2\n", - "\n", - "anyBSM\n", - "\n", - "\n", - "\n", - "0->2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - "MET\n", - "\n", - "\n", - "\n", - "1->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "1->5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - "MET\n", - "\n", - "\n", - "\n", - "2->4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - "higgs\n", - "\n", - "\n", - "\n", - "2->6\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "list(tp.expResult.getTxNames()[0].smsMap.keys())[0].draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "c4ca4d6c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "PV\n", - "\n", - "\n", - "\n", - "2\n", - "\n", - "N4\n", - "\n", - "\n", - "\n", - "0->2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "C2+\n", - "\n", - "\n", - "\n", - "0->1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - "N1\n", - "\n", - "\n", - "\n", - "2->4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - "higgs\n", - "\n", - "\n", - "\n", - "2->6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - "N1\n", - "\n", - "\n", - "\n", - "1->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - "W+\n", - "\n", - "\n", - "\n", - "1->5\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "PV\n", - "\n", - "\n", - "\n", - "2\n", - "\n", - "460.4\n", - "\n", - "\n", - "\n", - "0->2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "460.6\n", - "\n", - "\n", - "\n", - "0->1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - "86.9\n", - "\n", - "\n", - "\n", - "2->4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - "125.0\n", - "\n", - "\n", - "\n", - "2->6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - "86.9\n", - "\n", - "\n", - "\n", - "1->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - "80.0\n", - "\n", - "\n", - "\n", - "1->5\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sms1 = smsList[0]\n", - "sms1.draw()\n", - "sms1.draw(labelAttr='mass',attrUnit=GeV)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "571be06f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Combined analyses: ATLAS-SUSY-2013-11,CMS-SUS-13-013\n", - "Combined r value: 2.183E-02\n", - "Combined r value (expected): 2.183E-02\n", - "Likelihoods: L, L_max, L_SM = 1.385E-02, 1.401E-02, 1.394E-02\n", - "\n", - "\n", - " Combination of analyses done in 0.00m\n" - ] - } - ], - "source": [ - "t0 = time.time()\n", - "# Select a few results results for combination:\n", - "combineAnas = ['ATLAS-SUSY-2013-11', 'CMS-SUS-13-013']\n", - "selectedTheoryPreds = []\n", - "for tp in allPredictions:\n", - " expID = tp.analysisId()\n", - " if expID not in combineAnas:\n", - " continue\n", - " if tp.likelihood() is None:\n", - " continue\n", - " selectedTheoryPreds.append(tp)\n", - "# Make sure each analysis appears only once:\n", - "expIDs = [tp.analysisId() for tp in selectedTheoryPreds]\n", - "if len(expIDs) != len(set(expIDs)):\n", - " print(\"\\nDuplicated results when trying to combine analyses. Combination will be skipped.\")\n", - "# Only compute combination if at least two results were selected\n", - "elif len(selectedTheoryPreds) > 1:\n", - " combiner = TheoryPredictionsCombiner(selectedTheoryPreds)\n", - " combiner.computeStatistics()\n", - " llhd = combiner.likelihood()\n", - " lmax = combiner.lmax()\n", - " lsm = combiner.lsm()\n", - " print(\"\\n\\nCombined analyses:\", combiner.analysisId())\n", - " print(\"Combined r value: %1.3E\" % combiner.getRValue())\n", - " print(\"Combined r value (expected): %1.3E\" % combiner.getRValue(expected=True))\n", - " print(\"Likelihoods: L, L_max, L_SM = %10.3E, %10.3E, %10.3E\\n\" % (llhd, lmax, lsm))\n", - "\n", - "print(\"\\n Combination of analyses done in %1.2fm\" %((time.time()-t0)/60.))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "127fa69d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Coverage done in 0.77m\n" - ] - } - ], - "source": [ - "t0 = time.time()\n", - "# Find out missing topologies for sqrts=13*TeV:\n", - "uncovered = coverage.Uncovered(topDict, sqrts=13.*TeV)\n", - "print(\"\\n Coverage done in %1.2fm\" %((time.time()-t0)/60.))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4e4a9e3a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Total cross-section for missing topologies (fb): 1.120E+04\n", - "\n", - "\n", - "Total cross-section for missing topologies with displaced decays (fb): 0.000E+00\n", - "\n", - "\n", - "Total cross-section for missing topologies with prompt decays (fb): 1.399E+04\n", - "\n", - "\n", - "Total cross-section for topologies outside the grid (fb): 3.746E+03\n", - "\n" - ] - } - ], - "source": [ - "# First sort coverage groups by label\n", - "groups = sorted(uncovered.groups[:], key=lambda g: g.label)\n", - "# Print uncovered cross-sections:\n", - "for group in groups:\n", - " print(\"\\nTotal cross-section for %s (fb): %10.3E\\n\" % (group.description, group.getTotalXSec()))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6fa1c9d3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing topologies (up to 3):\n", - "Element: (PV > MET,MET,jet,jet,l,nu)\n", - "\tcross-section (fb): 1203.8783553949456\n", - "Element: (PV > MET,MET,jet,jet,ta,nu)\n", - "\tcross-section (fb): 600.4536496545026\n", - "Element: (PV > MET,MET,jet,jet,jet,jet,t,b,b,b)\n", - "\tcross-section (fb): 515.0638147985978\n" - ] - } - ], - "source": [ - "missingTopos = uncovered.getGroup('missing (prompt)')\n", - "# Print some of the missing topologies:\n", - "if missingTopos.finalStateSMS:\n", - " print('Missing topologies (up to 3):')\n", - " for genEl in missingTopos.finalStateSMS[:3]:\n", - " print('Element:', genEl)\n", - " print('\\tcross-section (fb):', genEl.missingX)\n", - "else:\n", - " print(\"No missing topologies found\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5f564ea6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "No displaced decays\n" - ] - } - ], - "source": [ - "missingDisplaced = uncovered.getGroup('missing (displaced)')\n", - "# Print elements with displaced decays:\n", - "if missingDisplaced.finalStateSMS:\n", - " print('\\nElements with displaced vertices (up to 2):')\n", - " for genEl in missingDisplaced.finalStateSMS[:2]:\n", - " print('Element:', genEl)\n", - " print('\\tcross-section (fb):', genEl.missingX)\n", - "else:\n", - " print(\"\\nNo displaced decays\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c77a2bae", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/parameters.ini b/parameters.ini index 5273e7bab..805348119 100644 --- a/parameters.ini +++ b/parameters.ini @@ -25,23 +25,23 @@ ncpus = 1 ;Give number of cores used when running in parallel (integer, -1 means #Select database analyses [database] -path = ../smodels-database ; URL to the database pickle file (it will be downloaded) +path = official ; URL to the database pickle file (it will be downloaded) #can be extended by: +fastlim (adds fastlim results), +superseded (adds superseded results), +nonaggregated (adds results with non-aggregated SRs in addition to the aggregated results), +full_llhds (replaces simplified HistFactory likelihoods by full ones). # examples: path = official+nonaggregated, path = official+full_llhds -analyses = ATLAS-SUSY-2019-08-grp ;Set all to use all analyses included in the database +analyses = all ;Set all to use all analyses included in the database #to use only specific analyses, give a list of the names separated by comma # analyses = CMS-PAS-SUS-13-008, CMS-SUS-13-013,ATLAS-CONF-2013-024,ATLAS-SUSY-2013-04 # Wildcards are understood as in shell-expansion of file names: * ? [] # Filter centre-of-mass energy with suffix beginning with a colon, in unum-style, like :13*TeV # Note that the asterisk in the suffix is not a wildcard. -txnames= TChiWH ;Set all to use all constraints included in the database +txnames= all ;Set all to use all constraints included in the database #to use only specific constraints, give a list of the names separated by comma #txnames = T2,T1,TChiWZ # Wildcards are understood as in shell-expansion of file names: * ? [] -dataselector= upperLimit ; Set all to use all upper limit and efficiency maps results in the database. Set to upperLimit (efficiencyMap) to use only UL (EM) results: +dataselector= all ; Set all to use all upper limit and efficiency maps results in the database. Set to upperLimit (efficiencyMap) to use only UL (EM) results: #dataselector = efficiencyMap #It can also be used to select specific datasets (signal regions) from efficiency map results. For the latter provide a list of the desired dataset ids #dataselector = SRA mCT150,SRA mCT200