{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing csky vs SkyLLH vs Skylab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. contents:: :local:\n", "\n", "Here, we compare the performance, results, and user experience of csky with SkyLLH and Skylab." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Global setup" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a source position with a small but nonzero TS for PS-7yr." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "src_ra, src_dec = np.radians(11), 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## csky setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We just have a couple of basic imports; we'll also configure matplotlib plotting:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import histlite as hl\n", "import csky as cy\n", "src = cy.sources(src_ra, src_dec)\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/mnt/lfs7/user/ssclafani/software/external/csky/csky/plotting.py:92: MatplotlibDeprecationWarning: Support for setting the 'text.latex.preamble' or 'pgf.preamble' rcParam to a list of strings is deprecated since 3.3 and will be removed two minor releases later; set it to a single string instead.\n", " r'\\SetSymbolFont{operators} {sans}{OT1}{cmss} {m}{n}'\n" ] } ], "source": [ "cy.plotting.mrichman_mpl()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll load up a previously cached `Analysis`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ana_dir = '/data/user/mrichman/csky_cache/ana'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Setting up Analysis for:\n", "IC40, IC59, IC79, IC86_2011, IC86_2012_2014\n", "Setting up IC40...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC40_MC.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC40_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC40_exp.npy ...\n", "Energy PDF Ratio Model...\n", " * gamma = 4.0000 ...\n", "Signal Acceptance Model...\n", " * gamma = 4.0000 ...\n", "Setting up IC59...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC59_MC.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC59_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC59_exp.npy ...\n", "Energy PDF Ratio Model...\n", " * gamma = 4.0000 ...\n", "Signal Acceptance Model...\n", " * gamma = 4.0000 ...\n", "Setting up IC79...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC79_MC.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC79_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC79_exp.npy ...\n", "Energy PDF Ratio Model...\n", " * gamma = 4.0000 ...\n", "Signal Acceptance Model...\n", " * gamma = 4.0000 ...\n", "Setting up IC86_2011...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC86_2011_MC.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC86_2011_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC86_2011_exp.npy ...\n", "<- /data/user/mrichman/csky_cache/ana/IC86_2011.subanalysis.version-003-p02.npy \n", "Setting up IC86_2012_2014...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC86_2012_MC.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC86_2012_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC86_2013_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/IC86_2014_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC86_2012_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC86_2013_exp.npy ...\n", "Reading /data/ana/analyses/ps_tracks/version-003-p02/GRL/IC86_2014_exp.npy ...\n", "Energy PDF Ratio Model...\n", " * gamma = 4.0000 ...\n", "Signal Acceptance Model...\n", " * gamma = 4.0000 ...\n", "Done.\n", "CPU times: user 3min 8s, sys: 29.7 s, total: 3min 37s\n", "Wall time: 3min 37s\n" ] } ], "source": [ "%time ana = cy.get_analysis(cy.selections.repo, 'version-003-p02', cy.selections.PSDataSpecs.ps_7yr, dir=ana_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here on, we'll work with this `Analysis` and use 20 cores for multiprocessing." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cy.CONF.update(dict(\n", " mp_cpus=20,\n", " ana=ana,\n", "))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SkyLLH setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For SkyLLH, we need some more imports:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from skyllh.core import multiproc\n", "from skyllh.core.random import RandomStateService\n", "from skyllh.physics.source import PointLikeSource\n", "from skyllh.core.timing import TimeLord\n", "\n", "from i3skyllh.datasets import data_samples\n", "from i3skyllh.analyses.IC170922A_wGFU import analysis\n", "\n", "multiproc.NCPU = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define the datasets:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset \"IC40\": v002p03\n", " { livetime = 376.360 days }\n", " Experimental data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC40_exp.npy\n", " MC data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC40_MC.npy\n", " GRL data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC40_exp.npy\n", "Dataset \"IC59\": v002p03\n", " { livetime = 353.578 days }\n", " Experimental data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC59_exp.npy\n", " MC data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC59_MC.npy\n", " GRL data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC59_exp.npy\n", "Dataset \"IC79\": v002p03\n", " { livetime = 316.045 days }\n", " Experimental data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC79_exp.npy\n", " MC data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC79_MC.npy\n", " GRL data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC79_exp.npy\n", "Dataset \"IC86, 2011\": v002p03\n", " { livetime = 332.963 days }\n", " Experimental data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC86_2011_exp.npy\n", " MC data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC86_2011_MC.npy\n", " GRL data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC86_2011_exp.npy\n", "Dataset \"IC86, 2012-2014\": v002p03\n", " { livetime = 1058.479 days }\n", " Experimental data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC86_2012_exp.npy\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC86_2013_exp.npy\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC86_2014_exp.npy\n", " MC data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/IC86_2012_MC.npy\n", " GRL data:\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC86_2012_exp.npy\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC86_2013_exp.npy\n", " /data/ana/analyses/ps_tracks/version-002-p03/GRL/IC86_2014_exp.npy\n" ] } ], "source": [ "#data_base_path='/home/mwolf/data/ana/analyses/'\n", "sample_seasons = [\n", " (\"PointSourceTracks_v002p03\", \"IC40\"),\n", " (\"PointSourceTracks_v002p03\", \"IC59\"),\n", " (\"PointSourceTracks_v002p03\", \"IC79\"),\n", " (\"PointSourceTracks_v002p03\", \"IC86, 2011\"),\n", " (\"PointSourceTracks_v002p03\", \"IC86, 2012-2014\"),\n", "]\n", "\n", "datasets = []\n", "for (sample, season) in sample_seasons:\n", " # Get the dataset from the correct dataset collection.\n", " dsc = data_samples[sample].create_dataset_collection()\n", " ds = dsc.get_dataset(season)\n", " print(ds)\n", " datasets.append(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can set up an `Analysis` of the SkyLLH type:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[==========================================================] 100% ELT 0h:02m:03s\n", "[==========================================================] 100% ELT 0h:01m:14s\n" ] } ], "source": [ "# Set up random number generation\n", "rss_seed = 1\n", "rss = RandomStateService(rss_seed)\n", "\n", "# Define the point source.\n", "source = PointLikeSource(src_ra, src_dec)\n", "\n", "# keep track of timing\n", "tl = TimeLord()\n", "\n", "with tl.task_timer('Creating analysis.'):\n", " sana = analysis.create_analysis(\n", " datasets,\n", " source,\n", " compress_data=True,\n", " tl=tl)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Executed tasks:\n", "[ Loading grl data from disk.] 0.0197656 sec\n", "[ Loading exp data from disk.] 0.102899 sec\n", "[ Loading mc data from disk.] 2.96356 sec\n", "[Selected only the experimental data that matches the GRL for dat] 0.00253534 sec\n", "[ Preparing data of dataset \"IC40\" by \"data_prep\".] 0.0415356 sec\n", "[ Preparing data of dataset \"IC40\" by \"add_azi_and_zen\".] 0.00108433 sec\n", "[ Appending IceCube-specific data fields to exp data.] 0.0157051 sec\n", "[ Appending IceCube-specific data fields to MC data.] 0.533087 sec\n", "[ Cleaning exp data.] 0.000311136 sec\n", "[ Cleaning MC data.] 0.0206943 sec\n", "[ Compressing exp data.] 0.00701237 sec\n", "[ Compressing MC data.] 0.272309 sec\n", "[ Asserting data format.] 0.000173092 sec\n", "[Selected only the experimental data that matches the GRL for dat] 0.00695467 sec\n", "[ Preparing data of dataset \"IC59\" by \"data_prep\".] 0.17718 sec\n", "[ Preparing data of dataset \"IC59\" by \"add_azi_and_zen\".] 0.00281072 sec\n", "[Selected only the experimental data that matches the GRL for dat] 0.0106368 sec\n", "[ Preparing data of dataset \"IC79\" by \"data_prep\".] 0.271917 sec\n", "[ Preparing data of dataset \"IC79\" by \"add_azi_and_zen\".] 0.00474 sec\n", "[Selected only the experimental data that matches the GRL for dat] 0.0106013 sec\n", "[ Preparing data of dataset \"IC86, 2011\" by \"data_prep\".] 0.152292 sec\n", "[ Preparing data of dataset \"IC86, 2011\" by \"add_azi_and_zen\".] 0.00223064 sec\n", "[Selected only the experimental data that matches the GRL for dat] 0.0344775 sec\n", "[ Preparing data of dataset \"IC86, 2012-2014\" by \"data_prep\".] 0.795044 sec\n", "[Preparing data of dataset \"IC86, 2012-2014\" by \"add_azi_and_zen\"] 0.02088 sec\n", "[ Creating analysis.] 197.409 sec\n" ] } ], "source": [ "print(tl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Underlying configuration**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up this SkyLLH Analysis requires some explicit setup that needs to be written out... someplace. Above we borrowed from `i3skyllh`'s included analysis script. The relevant contents of that script are as follows:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import argparse\n", "import logging\n", "import numpy as np\n", "\n", "from skyllh.core.progressbar import ProgressBar\n", "\n", "# Classes to define the source hypothesis.\n", "from skyllh.physics.source import PointLikeSource\n", "from skyllh.physics.flux import PowerLawFlux\n", "from skyllh.core.source_hypo_group import SourceHypoGroup\n", "from skyllh.core.source_hypothesis import SourceHypoGroupManager\n", "\n", "# Classes to define the fit parameters.\n", "from skyllh.core.parameters import (\n", " SingleSourceFitParameterMapper,\n", " FitParameter\n", ")\n", "\n", "# Classes for the minimizer.\n", "from skyllh.core.minimizer import Minimizer, LBFGSMinimizerImpl\n", "\n", "# Classes for utility functionality.\n", "from skyllh.core.config import CFG\n", "from skyllh.core.random import RandomStateService\n", "from skyllh.core.optimize import SpatialBoxEventSelectionMethod\n", "from skyllh.core.smoothing import BlockSmoothingFilter\n", "from skyllh.core.timing import TimeLord\n", "from skyllh.core.trialdata import TrialDataManager\n", "\n", "# Classes for defining the analysis.\n", "from skyllh.core.test_statistic import TestStatisticWilks\n", "from skyllh.core.analysis import (\n", " SpacialEnergyTimeIntegratedMultiDatasetSingleSourceAnalysis as Analysis\n", ")\n", "\n", "# Classes to define the background generation.\n", "from skyllh.core.scrambling import DataScrambler, UniformRAScramblingMethod\n", "from skyllh.i3.background_generation import FixedScrambledExpDataI3BkgGenMethod\n", "\n", "# Classes to define the detector signal yield tailored to the source hypothesis.\n", "from skyllh.i3.detsigyield import PowerLawFluxPointLikeSourceI3DetSigYieldImplMethod\n", "\n", "# Classes to define the signal and background PDFs.\n", "from skyllh.core.signalpdf import GaussianPSFPointLikeSourceSignalSpatialPDF\n", "from skyllh.i3.signalpdf import SignalI3EnergyPDFSet\n", "from skyllh.i3.backgroundpdf import (\n", " DataBackgroundI3SpatialPDF,\n", " DataBackgroundI3EnergyPDF\n", ")\n", "# Classes to define the spatial and energy PDF ratios.\n", "from skyllh.core.pdfratio import (\n", " SpatialSigOverBkgPDFRatio,\n", " Skylab2SkylabPDFRatioFillMethod\n", ")\n", "from skyllh.i3.pdfratio import I3EnergySigSetOverBkgPDFRatioSpline\n", "\n", "from skyllh.i3.signal_generation import PointLikeSourceI3SignalGenerationMethod\n", "\n", "# Analysis utilities.\n", "from skyllh.core.analysis_utils import (\n", " pointlikesource_to_data_field_array\n", ")\n", "\n", "# Logging setup utilities.\n", "from skyllh.core.debugging import (\n", " setup_logger,\n", " setup_console_handler,\n", " setup_file_handler\n", ")\n", "\n", "# The pre-defined data samples.\n", "from i3skyllh.datasets import data_samples\n", "\n", "def TXS_location():\n", " src_ra = np.radians(77.358)\n", " src_dec = np.radians(5.693)\n", " return (src_ra, src_dec)\n", "\n", "def create_analysis(\n", " datasets,\n", " source,\n", " refplflux_Phi0=1,\n", " refplflux_E0=1e3,\n", " refplflux_gamma=2,\n", " data_base_path=None,\n", " compress_data=False,\n", " keep_data_fields=None,\n", " tl=None,\n", " ppbar=None\n", "):\n", " \"\"\"Creates the Analysis instance for this particular analysis.\n", "\n", " Parameters:\n", " -----------\n", " datasets : list of Dataset instances\n", " The list of Dataset instances, which should be used in the\n", " analysis.\n", " source : PointLikeSource instance\n", " The PointLikeSource instance defining the point source position.\n", " refplflux_Phi0 : float\n", " The flux normalization to use for the reference power law flux model.\n", " refplflux_E0 : float\n", " The reference energy to use for the reference power law flux model.\n", " refplflux_gamma : float\n", " The spectral index to use for the reference power law flux model.\n", " data_base_path : str | None\n", " The base path of the data files (of all data samples).\n", " compress_data : bool\n", " Flag if the data should get converted from float64 into float32.\n", " keep_data_fields : list of str | None\n", " List of additional data field names that should get kept when loading\n", " the data.\n", " tl : TimeLord instance | None\n", " The TimeLord instance to use to time the creation of the analysis.\n", " ppbar : ProgressBar instance | None\n", " The instance of ProgressBar for the optional parent progress bar.\n", "\n", " Returns\n", " -------\n", " analysis : MultiDatasetTimeIntegratedSpacialEnergySingleSourceAnalysis\n", " The Analysis instance for this analysis.\n", " \"\"\"\n", " # Define the flux model.\n", " fluxmodel = PowerLawFlux(\n", " Phi0=refplflux_Phi0, E0=refplflux_E0, gamma=refplflux_gamma)\n", "\n", " # Define the fit parameter ns.\n", " fitparam_ns = FitParameter('ns', 0, 1e3, 15)\n", "\n", " # Define the gamma fit parameter.\n", " fitparam_gamma = FitParameter('gamma', valmin=1, valmax=4, initial=2)\n", "\n", " # Define the detector signal efficiency implementation method for the\n", " # IceCube detector and this source and fluxmodel.\n", " # The sin(dec) binning will be taken by the implementation method\n", " # automatically from the Dataset instance.\n", " gamma_grid = fitparam_gamma.as_linear_grid(delta=0.1)\n", " detsigyield_implmethod = PowerLawFluxPointLikeSourceI3DetSigYieldImplMethod(gamma_grid)\n", "\n", " # Define the signal generation method.\n", " sig_gen_method = PointLikeSourceI3SignalGenerationMethod()\n", "\n", " # Create a source hypothesis group manager.\n", " src_hypo_group_manager = SourceHypoGroupManager(\n", " SourceHypoGroup(source, fluxmodel, detsigyield_implmethod, sig_gen_method))\n", "\n", " # Create a source fit parameter mapper and define the fit parameters.\n", " src_fitparam_mapper = SingleSourceFitParameterMapper()\n", " src_fitparam_mapper.def_fit_parameter(fitparam_gamma)\n", "\n", " # Define the test statistic.\n", " test_statistic = TestStatisticWilks()\n", "\n", " # Define the data scrambler with its data scrambling method, which is used\n", " # for background generation.\n", " data_scrambler = DataScrambler(UniformRAScramblingMethod())\n", "\n", " # Create background generation method.\n", " bkg_gen_method = FixedScrambledExpDataI3BkgGenMethod(data_scrambler)\n", "\n", " # Define the event selection method for pure optimization purposes.\n", " event_selection_method = SpatialBoxEventSelectionMethod(\n", " src_hypo_group_manager, delta_angle=np.deg2rad(10))\n", "\n", " # Create the minimizer instance.\n", " minimizer = Minimizer(LBFGSMinimizerImpl())\n", "\n", " # Create the Analysis instance.\n", " analysis = Analysis(\n", " minimizer,\n", " src_hypo_group_manager,\n", " src_fitparam_mapper,\n", " fitparam_ns,\n", " test_statistic,\n", " bkg_gen_method,\n", " event_selection_method\n", " )\n", "\n", " # Add the data sets to the analysis.\n", " pbar = ProgressBar(len(datasets), parent=ppbar).start()\n", " for ds in datasets:\n", " # Load the data of the data set.\n", " data = ds.load_and_prepare_data(\n", " keep_fields=keep_data_fields, compress=compress_data, tl=tl)\n", "\n", " # Create a trial data manager and add the required data fields.\n", " tdm = TrialDataManager()\n", " tdm.add_source_data_field('src_array', pointlikesource_to_data_field_array)\n", "\n", " sin_dec_binning = ds.get_binning_definition('sin_dec')\n", " log_energy_binning = ds.get_binning_definition('log_energy')\n", "\n", " # Create the spatial PDF ratio instance for this dataset.\n", " spatial_sigpdf = GaussianPSFPointLikeSourceSignalSpatialPDF(\n", " dec_range=np.arcsin(sin_dec_binning.range))\n", " spatial_bkgpdf = DataBackgroundI3SpatialPDF(\n", " data.exp, sin_dec_binning)\n", " spatial_pdfratio = SpatialSigOverBkgPDFRatio(\n", " spatial_sigpdf, spatial_bkgpdf)\n", "\n", " # Create the energy PDF ratio instance for this dataset.\n", " smoothing_filter = BlockSmoothingFilter(nbins=1)\n", " energy_sigpdfset = SignalI3EnergyPDFSet(\n", " data.mc, log_energy_binning, sin_dec_binning, fluxmodel, gamma_grid,\n", " smoothing_filter, ppbar=pbar)\n", " energy_bkgpdf = DataBackgroundI3EnergyPDF(\n", " data.exp, log_energy_binning, sin_dec_binning, smoothing_filter)\n", " fillmethod = Skylab2SkylabPDFRatioFillMethod()\n", " energy_pdfratio = I3EnergySigSetOverBkgPDFRatioSpline(\n", " energy_sigpdfset, energy_bkgpdf,\n", " fillmethod=fillmethod,\n", " ppbar=pbar)\n", "\n", " analysis.add_dataset(ds, data, spatial_pdfratio, energy_pdfratio, tdm)\n", "\n", " pbar.increment()\n", " pbar.finish()\n", "\n", " analysis.llhratio = analysis.construct_llhratio(ppbar=ppbar)\n", "\n", " analysis.construct_signal_generator()\n", "\n", " return analysis\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Skylab setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Skylab, we include some more imports:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Could not import from icecube, coordinate operations will not be possible\n" ] } ], "source": [ "from skylab.ps_llh import PointSourceLLH, MultiPointSourceLLH\n", "from skylab.ps_injector import PointSourceInjector\n", "\n", "from skylab.llh_models import ClassicLLH, EnergyLLH\n", "from skylab.datasets import Datasets\n", "from skylab.sensitivity_utils import DeltaChiSquare\n", "\n", "GeV = 1\n", "TeV = 1000*GeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As far as I can tell, typical skylab usage entails creating a `config.py` containing a function like the following, which is a lightly edited version of the TXS 0506+056 configuration:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def config(seasons, seed = 1, scramble = True, e_range=(0,np.inf),\n", " g_range=[1.,4.], verbose = True, gamma = 2.0, E0 = 1*TeV,\n", " dec = None, remove = False, temporal_profile=None):\n", " r\"\"\" Configure multi season point source likelihood and injector.\n", "\n", " Parameters\n", " ----------\n", " seasons : list\n", " List of season names\n", " seed : int\n", " Seed for random number generator\n", "\n", " Returns\n", " -------\n", " multillh : MultiPointSourceLLH\n", " Multi year point source likelihood object\n", " inj : PointSourceInjector\n", " Point source injector object\n", " \"\"\"\n", "\n", " print(\"Scramble is %s\" % str(scramble))\n", "\n", " # store individual llh as lists to prevent pointer over-writing\n", " llh = []\n", "\n", " multillh = MultiPointSourceLLH(seed=seed, ncpu=10)\n", "\n", " # setup likelihoods\n", " if verbose: print(\"\\n seasons:\")\n", " for season in np.atleast_1d(seasons):\n", "\n", " sample = season[0]\n", " name = season[1]\n", "\n", " exp, mc, livetime = Datasets[sample].season(name)\n", " sinDec_bins = Datasets[sample].sinDec_bins(name)\n", " energy_bins = Datasets[sample].energy_bins(name)\n", "\n", " mc = mc[mc[\"logE\"] > 1.]\n", "\n", " if verbose:\n", " print(\" - % 15s : % 15s\" % (sample, name))\n", " vals = (livetime, exp.size, min(exp['time']), max(exp['time']))\n", " print(\" (livetime %7.2f days, %6d events, mjd0 %.2f, mjd1 %.2f)\" % vals)\n", "\n", " llh_model = EnergyLLH(twodim_bins = [energy_bins, sinDec_bins], allow_empty=True,\n", " bounds = g_range, seed = 2.0, kernel=0, ncpu=10,\n", " fill_method='classic')\n", "\n", " llh.append( PointSourceLLH(exp, mc, livetime, mode = \"box\",\n", " scramble = scramble, llh_model = llh_model,\n", " nsource_bounds=(0., 1e3), nsource=15.,\n", " temporal_profile=temporal_profile) )\n", "\n", " multillh.add_sample(sample+\" : \"+name, llh[-1])\n", "\n", " # save a little RAM by removing items copied into LLHs\n", " del exp, mc\n", "\n", " # END for (season)\n", "\n", " #######\n", " # LLH # (end)\n", " #######\n", "\n", " if verbose:\n", " print(\"\\n fitted spectrum:\")\n", " vals = (E0/TeV)\n", " print(\" - dN/dE = A (E / %.1f TeV)^-index TeV^-1cm^-2s^-1\" % vals)\n", " print(\" - index is *fit*\")\n", "\n", " # return only llh if no dec specified\n", " if dec is None: return multillh\n", "\n", " inj = PointSourceInjector(gamma = gamma, E0 = E0, seed = seed, e_range=e_range)\n", " inj.fill(dec, multillh.exp, multillh.mc, multillh.livetime)\n", "\n", " ############\n", " # INJECTOR # (end)\n", " ############\n", "\n", " if verbose:\n", " print(\"\\n injected spectrum:\")\n", " print(\" - %s\" % inj.spectrum)\n", "\n", " return (multillh, inj)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, we'll set things up for PS-7yr:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "seasons = [ \n", " [\"PointSourceTracks\", \"IC40\"],\n", " [\"PointSourceTracks\", \"IC59\"],\n", " [\"PointSourceTracks\", \"IC79\"],\n", " [\"PointSourceTracks\", \"IC86, 2011\"],\n", " [\"PointSourceTracks_v002p03\", \"IC86, 2012-2014\"],\n", "]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scramble is False\n", "\n", " seasons:\n", " - PointSourceTracks : IC40\n", " (livetime 376.36 days, 36900 events, mjd0 54562.38, mjd1 54971.13)\n", " - PointSourceTracks : IC59\n", " (livetime 353.58 days, 107011 events, mjd0 54971.16, mjd1 55347.28)\n", " - PointSourceTracks : IC79\n", " (livetime 316.05 days, 93133 events, mjd0 55348.31, mjd1 55694.41)\n", " - PointSourceTracks : IC86, 2011\n", " (livetime 332.96 days, 136244 events, mjd0 55694.99, mjd1 56062.42)\n", " - PointSourceTracks_v002p03 : IC86, 2012-2014\n", " (livetime 1058.48 days, 338590 events, mjd0 56043.43, mjd1 57160.04)\n", "\n", " fitted spectrum:\n", " - dN/dE = A (E / 1.0 TeV)^-index TeV^-1cm^-2s^-1\n", " - index is *fit*\n", "\n", " injected spectrum:\n", " - dN/dE = 1.00e+00 * (E / 1.00e+03 GeV)^-2.00 [GeV^-1cm^-2s^-1]\n", "CPU times: user 12.1 s, sys: 12.5 s, total: 24.6 s\n", "Wall time: 52.1 s\n" ] } ], "source": [ "%time multillh, inj = config(seasons, scramble=False, dec=src_dec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unblind Source Coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unblinding with csky:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.6076973920640549, {'gamma': 4.0, 'ns': 6.7999192587146515})\n", "CPU times: user 33.5 ms, sys: 5.08 ms, total: 38.6 ms\n", "Wall time: 38.5 ms\n" ] } ], "source": [ "tr = cy.get_trial_runner(src=cy.sources(src_ra, src_dec))\n", "%time print(tr.get_one_fit(TRUTH=True, flat=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unblinding with SkyLLH:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.6025844403658499, {'ns': 6.770293031234257, 'gamma': 4.0})\n", "CPU times: user 65.9 ms, sys: 4.99 ms, total: 70.9 ms\n", "Wall time: 70.9 ms\n" ] } ], "source": [ "sana.change_source(PointLikeSource(src_ra, src_dec))\n", "%time print(sana.unblind(rss)[:2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unblinding with Skylab:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.5860533647682029, {'nsignal': 6.607600450179375, 'gamma': 4.0})\n", "CPU times: user 138 ms, sys: 49.1 ms, total: 187 ms\n", "Wall time: 185 ms\n" ] } ], "source": [ "%time print(multillh.fit_source(src_ra, src_dec))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trial timing benchmarks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll perform 100 trials on a single core, using each library:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performing 100 background trials using 1 core:\n", " 100/100 trials complete. \n", "[==========================================================] 100% ELT 0h:00m:16s\n" ] } ], "source": [ "timer = cy.timing.Timer()\n", "n_trials = 100\n", "\n", "with timer.time('csky'):\n", " csky_100_trials = tr.get_many_fits(n_trials, mp_cpus=1)\n", "\n", "with timer.time('skyllh'):\n", " skyllh_100_trials = sana.do_trials(rss, n_trials, ncpu=1)\n", "\n", "with timer.time('skylab'):\n", " multillh.ncpu = 1\n", " skylab_100_trials = multillh.do_trials(n_trials, src_ra=src_ra, src_dec=src_dec)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "csky | 0:00:03.784612\n", "skyllh | 0:00:15.904761\n", "skylab | 0:00:17.926248 \n", "\n", "csky | 1.0000\n", "skyllh | 4.2025\n", "skylab | 4.7366 \n", "\n", "csky | 0.2380\n", "skyllh | 1.0000\n", "skylab | 1.1271 \n", "\n", "csky | 0.2111\n", "skyllh | 0.8872\n", "skylab | 1.0000\n" ] } ], "source": [ "print(timer, '\\n')\n", "print(timer.ratios('csky'), '\\n')\n", "print(timer.ratios('skyllh'), '\\n')\n", "print(timer.ratios('skylab'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background trials comparison" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we'll do 10k background trials using 10 cores -- partially as a timing benchmark, but mostly so we can compare those distributions." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performing 10000 background trials using 10 cores:\n", " 10000/10000 trials complete. \n", "[==========================================================] 100% ELT 0h:03m:56s\n" ] } ], "source": [ "timer = cy.timing.Timer()\n", "n_trials = 10000\n", "mp_cpus = 10\n", "with timer.time('csky'):\n", " csky_10k_trials = tr.get_many_fits(10000, mp_cpus=mp_cpus)\n", "\n", "with timer.time('skyllh'):\n", " skyllh_10k_trials = sana.do_trials(rss, 10000, ncpu=mp_cpus)\n", "\n", "with timer.time('skylab'):\n", " multillh.ncpu = mp_cpus\n", " skylab_10k_trials = cy.utils.Arrays(multillh.do_trials(10000, src_ra=src_ra, src_dec=src_dec))\n", " skylab_10k_trials['ts'], skylab_10k_trials['ns'] = skylab_10k_trials.TS, skylab_10k_trials.nsignal" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "csky | 0:00:57.109582\n", "skyllh | 0:03:56.034262\n", "skylab | 0:04:32.805918 \n", "\n", "csky | 1.0000\n", "skyllh | 4.1330\n", "skylab | 4.7769 \n", "\n", "csky | 0.2420\n", "skyllh | 1.0000\n", "skylab | 1.1558 \n", "\n", "csky | 0.2093\n", "skyllh | 0.8652\n", "skylab | 1.0000\n" ] } ], "source": [ "print(timer, '\\n')\n", "print(timer.ratios('csky'), '\\n')\n", "print(timer.ratios('skyllh'), '\\n')\n", "print(timer.ratios('skylab'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We fit each distribution as a `Chi2TSD`." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "bg_csky = cy.dists.Chi2TSD(csky_10k_trials)\n", "bg_skyllh = cy.dists.Chi2TSD(cy.utils.Arrays(skyllh_10k_trials))\n", "bg_skylab = cy.dists.Chi2TSD(cy.utils.Arrays(skylab_10k_trials))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can compare the TS, $n_s$, and $\\gamma$ distributions:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "plt.matplotlib.style.use('default')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "cy.plotting.mrichman_mpl()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 3, figsize=(9,3), dpi=200)\n", "names = 'ts ns gamma'.split()\n", "labels = 'csky skyllh skylab'.split()\n", "all_bgs = bg_csky, bg_skyllh, bg_skylab\n", "binss = [np.r_[:20.1:.5], np.r_[:70.1:2], np.r_[1:4.01:.05]]\n", "for (b, label) in zip(all_bgs, labels):\n", " for (name, ax, bins) in zip(names, axs, binss):\n", " v = b.trials[name]\n", " if name == 'gamma':\n", " v = v[b.trials.ns > 0]\n", " h = hl.hist(v, bins=bins)\n", " if label == 'csky':\n", " color, lw, alpha = 'k', 2, .5\n", " elif label == 'skyllh':\n", " color, lw, alpha = 'C0', 1.25, .5\n", " else:\n", " color, lw, alpha = 'C1', 1.25, .5\n", " hl.plot1d(ax, h, crosses=True, label=label,\n", " color=color, lw=lw, alpha=alpha)\n", " if name == 'ts':\n", " norm = h.integrate().values\n", " x = np.linspace(.5, bins[-1], 300)\n", " ax.plot(x, norm * b.pdf(x), color=color, lw=.5)\n", " ax.set_xlabel(name)\n", "axs[0].set_ylim(1e-1, 2e4)\n", "axs[0].semilogy()\n", "axs[1].legend(loc='upper center')\n", "#axs[1].semilogy()\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fits themselves came out like so:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "csky Chi2TSD(10000 trials, eta=0.594, ndof=1.031, median=0.052 (from fit 0.046))\n", "skyllh Chi2TSD(10000 trials, eta=0.524, ndof=1.389, median=0.022 (from fit 0.019))\n", "skylab Chi2TSD(10000 trials, eta=0.536, ndof=1.283, median=0.023 (from fit 0.024))\n" ] } ], "source": [ "for (label,bg) in zip(labels,all_bgs):\n", " print('{:10} {}'.format(label, bg))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The $(n_s,\\gamma)$ distributions are:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 3, figsize=(9,3), dpi=200, sharey=True)\n", "for (b, label, ax) in zip(all_bgs, labels, axs):\n", " i = b.trials.ns > 0\n", " h = hl.hist((b.trials.ns[i], b.trials.gamma[i]),\n", " bins=(np.r_[:40.01:1], np.r_[1:4.01:.1])).normalize()\n", " h = hl.kde((b.trials.ns[i], b.trials.gamma[i]), range=h.range)\n", " hl.plot2d(ax, h, vmax = 0.09, cmap='viridis')\n", " ax.set_title(label)\n", " ax.set_xlabel(r'ns')\n", "axs[0].set_ylabel('gamma')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or as raw histograms rather than KDE," ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 3, figsize=(9,3), dpi=200)\n", "for (b, label, ax) in zip(all_bgs, labels, axs):\n", " i = b.trials.ns > 0\n", " h = hl.hist((b.trials.ns[i], b.trials.gamma[i]),\n", " bins=(np.r_[:40.01:1], np.r_[1:4.01:.1])).normalize()\n", " #h = hl.kde((b.trials.ns[i], b.trials.gamma[i]), range=h.range, kernel=(.02,.01))\n", " hl.plot2d(ax, h, vmax = 0.09, cmap='viridis')\n", " ax.set_title(label)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sensitivity estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sensitivity estimation can be done in a number of ways. csky provides an algorithm which performs an initial scan to identify the region of interest, and then performs batches of trials on a grid of signal strengths until the desired tolerance or maximum batch size is reached:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "timer = cy.timing.Timer()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start time: 2019-07-25 12:26:57.631627\n", "Using 10 cores.\n", "* Starting initial scan for 90% of 50 trials with TS >= 0.052...\n", " n_sig = 1.000 ... frac = 0.68000\n", " n_sig = 2.000 ... frac = 0.74000\n", " n_sig = 3.000 ... frac = 0.78000\n", " n_sig = 4.000 ... frac = 0.92000\n", "* Generating batches of 1000 trials...\n", "n_trials | n_inj 0.00 1.60 3.20 4.80 6.40 8.00 | n_sig(relative error)\n", "1000 | 49.9% 73.3% 85.1% 92.0% 95.2% 98.2% | 4.214 (+/- 4.7%) [spline]\n", "End time: 2019-07-25 12:27:51.538601\n", "Elapsed time: 0:00:53.906974\n" ] } ], "source": [ "with timer.time('csky'):\n", " tr.find_n_sig(\n", " bg_csky.median(), 0.9, tol=.03,\n", " batch_size=1000, max_batch_size=1000, n_sig_step=1, mp_cpus=10)['n_sig']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SkyLLH provides an algorithm that I haven't fully understood yet -- but given that it appears to run on a single core, the timing is potentially impressive:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "from skyllh.core.analysis_utils import estimate_sensitivity" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[==========================================================] 100% ELT 0h:00m:23s\n", "[==========================================================] 100% ELT 0h:00m:21s\n", "[==========================================================] 100% ELT 0h:00m:20s\n", "[==========================================================] 100% ELT 0h:00m:19s\n", "[==========================================================] 100% ELT 0h:00m:40s\n" ] } ], "source": [ "with timer.time('skyllh'):\n", " sens_skyllh = estimate_sensitivity(sana, rss, h0_ts_vals=skylab_10k_trials['ts'], eps_p=.03)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4.84375, 0.0)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sens_skyllh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Skylab provides an algorithm similar to that of csky:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from skylab.sensitivity_utils import estimate_sensitivity as skylab_estimate_sensitivity" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scanning injected event numbers [0.0, 8.0] ...\n", " [ 0] ni 0.00, TS 0.89, sigma 0.94 +/- 0.22 (20 trials/1.22 sec)\n", " [ 1] ni 1.60, TS 2.83, sigma 1.68 +/- 0.22 (20 trials/1.58 sec)\n", " [ 2] ni 3.20, TS 4.24, sigma 2.06 +/- 0.22 (20 trials/1.36 sec)\n", " [ 3] ni 4.80, TS 6.87, sigma 2.62 +/- 0.22 (20 trials/1.38 sec)\n", " [ 4] ni 6.40, TS 7.25, sigma 2.69 +/- 0.22 (20 trials/1.43 sec)\n", " [ 5] ni 8.00, TS 12.71, sigma 3.56 +/- 0.22 (20 trials/3.04 sec)\n", "\n", "Initial Guesses ...\n", " Sensitivity ni 3.95, flux @ 1.0e+03 GeV is 3.27e-16 GeV^-1cm^-2s^-1\n", " Discovery ni 13.18, flux @ 1.0e+03 GeV is 1.09e-15 GeV^-1cm^-2s^-1\n", "\n", "Sensitivity Threshold ...\n", " Looking for 90% > Median TS (Median TS = 0.02)\n", " Scanning injected event range [0.00, 8.79]\n", " [ 0] ni 0.00, n 491, ntot 1000 (25.23 sec)\n", " [ 1] ni 1.76, n 739, ntot 1000 (31.81 sec)\n", " [ 2] ni 3.51, n 856, ntot 1000 (37.83 sec)\n", " [ 3] ni 5.27, n 935, ntot 1000 (36.75 sec)\n", " [ 4] ni 7.03, n 954, ntot 1000 (39.11 sec)\n", " [ 5] ni 8.79, n 980, ntot 1000 (43.94 sec)\n", "\n", "Best fit:\n", " ndf ----------- 4.05e+00 +/- 4.08e-01\n", " x0 ------------ -3.41e+00 +/- 4.87e-01\n", " reduced chi2 -- 1.45 (dof 4)\n", " ni ------------ 4.44 +/- 0.16 (p 0.90)\n", " flux ---------- 3.67e-16 +/- 1.35e-17 GeV^-1cm^-2s^-1\n", "\n" ] } ], "source": [ "with timer.time('skylab'):\n", " multillh.ncpu = 10\n", " skylab_sens = skylab_estimate_sensitivity(\n", " multillh, inj,\n", " src_ra=src_ra, src_dec=src_dec,\n", " ts_val=[bg_skylab.median(), 1], do_disc=False,\n", " ni_bounds=[0, 8])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "csky | 0:00:53.908107\n", "skyllh | 0:02:03.419458\n", "skylab | 0:03:51.849178\n" ] } ], "source": [ "print(timer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Closing remarks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have compared the basic performance, results, and user experience of csky with SkyLLH and Skylab. Here are some conclusions:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* For our single core test, the csky:skyllh:skylab time elapsed ratio was 1:4.2025:4.7366. csky is clearly the fastest.\n", "* For our 10 core test, the timing ratio was similar.\n", "* The skyllh sensitivity estimation algorithm appears to be very fast." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The background TS, $n_s$, $\\gamma$, and joint $(n_s,\\gamma)$ distributions suggest that csky is better at fitting small signals, especially for spectra running into the limits $\\gamma\\leq1$ or $\\gamma\\geq4$. This may be due to its initial scan in $\\gamma$ prior to the full $(n_s,\\gamma)$ fit.\n", "* csky also uses a denser default $\\gamma$ spacing of 0.0625, as opposed to 0.1. This naturally results in smoother $\\gamma$ distributions.\n", "* Otherwise the three libraries give good agreement on numerical results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### User experience" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, the following are just one person's (M. Richman) observations and opinions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**csky**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Setup is extremely concise, not only for the single point source analysis tested here, but even for relatively complex likelihood and signal injection definitions.\n", "* Execution is the fastest out of tools tested.\n", "* Special-cased progress indicators appear for various stages: `Analysis` construction, trial generation, sensitivity estimation, [not shown here] skymap calculation.\n", "* `Analysis` cache-to-disk greatly reduces computational setup time.\n", "* Main advantage is probably the balance of flexibility and organic design. The PDF / signal acceptance / likelihood / minimizer framework was carefully thought out, but ultimately is small; specific use cases have been only one layer above this foundation, on an as-needed basis, and with C++ optimizations also added on an as-needed basis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**SkyLLH**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Setup is relatively verbose, but if the user spells out the configuration herself, there can be no question what is the behavior of any aspect of the likelihood.\n", "* Execution is faster than skylab.\n", "* Generalized progress bars offer helpful estimate of estimated remaining time (but the user must consult the code to know what is happening, as no detailed feedback is given).\n", "* `Analysis` cache-to-disk would be very helpful in reducing setup time, but I don't know if it is implemented.\n", "* Main advantage is probably the highly flexible design. The analysis you want to do might not already be implemented, but if it is not, then hopefully it is fairly obvious where the required pieces need to go." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Skylab**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Amount of code required for setup is somewhat intermediate compared to csky or skyllh.\n", "* Execution is slowest of those tested, but profiling [not shown here] suggests that at least for time-integrated analysis, the main bottlenecks are necessary calculations: space and energy PDFs, minimizer iterations. It is to SkyLLH's credit that it can run faster while maintaining the goals of numerical equivalence with skylab using pure python.\n", "* No feedback during trials.\n", "* Sensitivity estimation — I wish it could automatically deduce `ni_bounds` and wouldn't make plots by default.\n", "* Multiprocessing for `PointSourceLLH` construction helps with setup time, but can't beat cache-to-disk (which I believe is supposed to be possible with Skylab; I just don't know how to do it).\n", "* Main advantage of Skylab is probably the amount of use and number of users it has had to date. For many analysis types, known-working examples are readily available." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Beyond these tests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've worked through the basics here, but there's a lot more to cover, including:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* all-sky scans (at least csky and Skylab have specialized tools for this)\n", "* time-dependent analysis (csky currently most flexible here so far)\n", "* spatial template analysis (csky can work with per-pixel spectra, or fit the spectrum for space-only templates)\n", "* spatial prior analysis (most developed in Skylab, basics mostly available in csky as well)\n", "* modified PDF constructions, e.g. joint space+energy (already available, I think, in SkyLLH)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 4 }