{ "cells": [ { "cell_type": "markdown", "id": "golden-liability", "metadata": {}, "source": [ "# Support Moments Trial 1\n", "This notebook was used to generate the figures for gluex-doc-6084, \"Support vector moments of acceptance-corrected angular distributions from multi-particle experimental data\". The data used for this trial are served over xrootd from root://cn440.storrs.hpc.uconn.edu/Gluex/resilient/simulation/moments-6-2023. At the present time, the Jlab jupyterhub servers are blocked from accessing the internet, so these data have been copied to the following directory on the Jlab work disk. If you are running this notebook on your local laptop, you may need to change the setting of datadir to point to a location that is reachable on your platform." ] }, { "cell_type": "code", "execution_count": 1, "id": "ignored-arena", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Welcome to JupyROOT 6.22/06\n", "datadir is accessible, you can continue\n" ] } ], "source": [ "import ROOT\n", "import h5py\n", "import numpy as np\n", "import sys\n", "import os\n", "from concurrent.futures import ThreadPoolExecutor\n", "import uproot\n", "\n", "upcache = {}\n", "\n", "#datadir = \"root://cn440.storrs.hpc.uconn.edu/Gluex/resilient/simulation/moments-6-2023\"\n", "datadir = \"/work/halld/jonesrt/simulation/moments-6-2023\"\n", "if os.path.isdir(datadir):\n", " print(\"datadir is accessible, you can continue\")\n", "else:\n", " print(\"datadir is not accessible, please change datadir to point to a reachable copy of the trial data\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "responsible-solomon", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n", "Collecting git+https://github.com/rjones30/gluex_moments_analysis.git\n", " Cloning https://github.com/rjones30/gluex_moments_analysis.git to /tmp/pip-req-build-ck7f1xhf\n", " Running command git clone -q https://github.com/rjones30/gluex_moments_analysis.git /tmp/pip-req-build-ck7f1xhf\n", "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from gluex-moments-analysis==0.1.18) (1.20.0)\n", "Requirement already satisfied: awkward in ./.local/lib/python3.8/site-packages (from gluex-moments-analysis==0.1.18) (2.3.1)\n", "Requirement already satisfied: uproot in /opt/conda/lib/python3.8/site-packages (from gluex-moments-analysis==0.1.18) (4.1.9)\n", "Requirement already satisfied: h5py in /opt/conda/lib/python3.8/site-packages (from gluex-moments-analysis==0.1.18) (3.1.0)\n", "Requirement already satisfied: scipy in /opt/conda/lib/python3.8/site-packages (from gluex-moments-analysis==0.1.18) (1.6.0)\n", "Requirement already satisfied: awkward-cpp==21 in ./.local/lib/python3.8/site-packages (from awkward->gluex-moments-analysis==0.1.18) (21)\n", "Requirement already satisfied: importlib-resources in ./.local/lib/python3.8/site-packages (from awkward->gluex-moments-analysis==0.1.18) (6.0.0)\n", "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from awkward->gluex-moments-analysis==0.1.18) (20.9)\n", "Requirement already satisfied: typing-extensions>=4.1.0 in ./.local/lib/python3.8/site-packages (from awkward->gluex-moments-analysis==0.1.18) (4.7.1)\n", "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources->awkward->gluex-moments-analysis==0.1.18) (3.4.0)\n", "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->awkward->gluex-moments-analysis==0.1.18) (2.4.7)\n", "Requirement already satisfied: setuptools in /opt/conda/lib/python3.8/site-packages (from uproot->gluex-moments-analysis==0.1.18) (49.6.0.post20210108)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install --user git+https://github.com/rjones30/gluex_moments_analysis.git" ] }, { "cell_type": "code", "execution_count": 3, "id": "reverse-hygiene", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: >>> import buildMomentsMatrix as bmm\n", " >>> bmm.open('etapi0_moments_0.root:etapi0_moments')\n", " >>> acc_events = bmm.select_events(massEtaPi0_limits=(1.2,1.5), abst_limits=(0,0.2))\n", " >>> M,Mvar = bmm.buildMomentsMatrix_sequential(acc_events)\n", " >>> bmm.open('etapi0_moments_1.root:etapi0_moments')\n", " >>> acc_events2 = bmm.select_events(massEtaPi0_limits=(1.2,1.5), abst_limits=(0,0.2))\n", " >>> M2,Mvar2 = bmm.buildMomentsMatrix_threaded(acc_events2)\n", " >>> M += M2\n", " >>> Mvar += Mvar2\n", " >>> acc_events += acc_events2\n", " >>> # more of the above, till all accepted moments trees are read ...\n", " >>> bmm.open('generated_moments.root:etapi0_moments')\n", " >>> gen_events=bmm.select_events(massEtaPi0_limits=(1.2,1.5), abst_limits=(0,0.2))\n", " >>> outfile = 'save_moments_matrix.h5')\n", " >>> bmm.save_output(M, Mvar, acc_events, gen_events, outfile)\n", "usage: >>> import analyzeMomentsMatrix.py as ana\n", " >>> ana.open(\"filename.h5\")\n", " >>> h = ana.histogram_M_diagonal()\n", " >>> h = ana.histogram_M_diagonal_GJ0()\n", " >>> h = ana.histogram_M_diagonal_GJ0_Eta0()\n", " >>> h = ana.histogram_M_offdiagonal()\n", " >>> h = ana.histogram_eigenvalues(name, title, w)\n", " >>> h = ana.histogram_moments(name, title, moments, errors)\n", " >>> h = ana.analyze_moments(massEtaPi0_limits=(0,99), abst_limits=(0,99),\n", " sample_subset=range(10), acceptance_subset=range(10),\n", " model=1, Mmatrix=\"Msaved.h5\")\n", " >>> kinbins = ana.standard_kinematic_bins()\n", " >>> h = ana.model1_corrected_moments()\n", " >>> cmom,ccov,chists = ana.apply_constraints(S, moments, covariance, hists)\n", " >>> h = ana.histogram_moments_correlations()\n", "make: 'C_buildMomentsMatrix.so' is up to date.\n" ] } ], "source": [ "import gluex_moments_analysis.analyzeMomentsMatrix as ana\n", "ana.workdir = datadir" ] }, { "cell_type": "markdown", "id": "scenic-tribute", "metadata": {}, "source": [ "## Figure 1. \n", "The Monte Carlo sample used in this trial was generated for the reaction γp→ηπ0p with a broad invariant mass distribution of the ηπ0 system peaking at 1.5 GeV. The t-slope for the reaction was set to 1 GeV2. To plot the generated distributions in these variables, I access a root tree named etapi0_moments that was produced using the code in generated_moments.C (see github repository containing all code used in this trial at https://github.com/rjones30/gluex_moemnts_analysis.git) running over the full set of 100 million generated ηπ0p events in chunks of 10 million event. The output trees are saved in generated_moments_x10_n.root where n=0..9. Here I list the contents of all columns in the etapi0_moments tree." ] }, { "cell_type": "code", "execution_count": 4, "id": "through-archives", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "******************************************************************************\n", "*Tree :etapi0_moments: eta pi0 moments *\n", "*Entries : 10000000 : Total = 43082892947 bytes File Size = 32293475890 *\n", "* : : Tree compression factor = 1.33 *\n", "******************************************************************************\n", "*Br 0 :runNo : runNo/L *\n", "*Entries : 10000000 : Total Size= 80110371 bytes File Size = 561047 *\n", "*Baskets : 1079 : Basket Size= 106496 bytes Compression= 142.75 *\n", "*............................................................................*\n", "*Br 1 :eventNo : eventNo/L *\n", "*Entries : 10000000 : Total Size= 80112537 bytes File Size = 15504090 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 5.17 *\n", "*............................................................................*\n", "*Br 2 :weight : weight/D *\n", "*Entries : 10000000 : Total Size= 80111454 bytes File Size = 473850 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 169.02 *\n", "*............................................................................*\n", "*Br 3 :sqrts : sqrts/D *\n", "*Entries : 10000000 : Total Size= 80110371 bytes File Size = 35407379 *\n", "*Baskets : 1079 : Basket Size= 106496 bytes Compression= 2.26 *\n", "*............................................................................*\n", "*Br 4 :abst : abst/D *\n", "*Entries : 10000000 : Total Size= 80109288 bytes File Size = 76238885 *\n", "*Baskets : 1079 : Basket Size= 106496 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 5 :massEtaPi0 : massEtaPi0/D *\n", "*Entries : 10000000 : Total Size= 80115786 bytes File Size = 76280867 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 6 :massEta : massEta/D *\n", "*Entries : 10000000 : Total Size= 80112537 bytes File Size = 59373360 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.35 *\n", "*............................................................................*\n", "*Br 7 :massPi0 : massPi0/D *\n", "*Entries : 10000000 : Total Size= 80112537 bytes File Size = 63990494 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.25 *\n", "*............................................................................*\n", "*Br 8 :phiR : phiR/D *\n", "*Entries : 10000000 : Total Size= 80109288 bytes File Size = 77640622 *\n", "*Baskets : 1079 : Basket Size= 106496 bytes Compression= 1.03 *\n", "*............................................................................*\n", "*Br 9 :thetaGJ : thetaGJ/D *\n", "*Entries : 10000000 : Total Size= 80112537 bytes File Size = 76515691 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 10 :phiGJ : phiGJ/D *\n", "*Entries : 10000000 : Total Size= 80110371 bytes File Size = 77641688 *\n", "*Baskets : 1079 : Basket Size= 106496 bytes Compression= 1.03 *\n", "*............................................................................*\n", "*Br 11 :thetaEta : thetaEta/D *\n", "*Entries : 10000000 : Total Size= 80113620 bytes File Size = 76517295 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 12 :phiEta : phiEta/D *\n", "*Entries : 10000000 : Total Size= 80111454 bytes File Size = 77641646 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.03 *\n", "*............................................................................*\n", "*Br 13 :thetaPi0 : thetaPi0/D *\n", "*Entries : 10000000 : Total Size= 80113620 bytes File Size = 76516665 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 14 :phiPi0 : phiPi0/D *\n", "*Entries : 10000000 : Total Size= 80111454 bytes File Size = 77642351 *\n", "*Baskets : 1079 : Basket Size= 107008 bytes Compression= 1.03 *\n", "*............................................................................*\n", "*Br 15 :momentsGJ : Int_t momentsGJ/I[0,169] *\n", "*Entries : 10000000 : Total Size= 40114596 bytes File Size = 326310 *\n", "*Baskets : 1078 : Basket Size= 101376 bytes Compression= 122.87 *\n", "*............................................................................*\n", "*Br 16 :momentsEta : Int_t momentsEta/I[0,169] *\n", "*Entries : 10000000 : Total Size= 40115678 bytes File Size = 327388 *\n", "*Baskets : 1078 : Basket Size= 101376 bytes Compression= 122.47 *\n", "*............................................................................*\n", "*Br 17 :momentsPi0 : Int_t momentsPi0/I[0,169] *\n", "*Entries : 10000000 : Total Size= 40115678 bytes File Size = 327388 *\n", "*Baskets : 1078 : Basket Size= 101376 bytes Compression= 122.47 *\n", "*............................................................................*\n", "*Br 18 :YmomGJ : YmomGJ[momentsGJ]/D *\n", "*Entries :10000000 : Total Size=13560164704 bytes File Size = 13007242135 *\n", "*Baskets : 1480 : Basket Size= 14600363 bytes Compression= 1.04 *\n", "*............................................................................*\n", "*Br 19 :YmomEta : YmomEta[momentsEta]/D *\n", "*Entries :10000000 : Total Size= 7320265648 bytes File Size = 6995791028 *\n", "*Baskets : 2368 : Basket Size= 6822400 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 20 :YmomPi0 : YmomPi0[momentsPi0]/D *\n", "*Entries :10000000 : Total Size= 7320265648 bytes File Size = 6995796315 *\n", "*Baskets : 2368 : Basket Size= 6822400 bytes Compression= 1.05 *\n", "*............................................................................*\n", "*Br 21 :model1moment : model1moment[momentsGJ]/D *\n", "*Entries :10000000 : Total Size=13560173608 bytes File Size = 4425499224 *\n", "*Baskets : 1480 : Basket Size= 14600363 bytes Compression= 3.06 *\n", "*............................................................................*\n" ] } ], "source": [ "fgen0 = ROOT.TFile(datadir + \"/generated_moments_x10_0.root\")\n", "gen_tree = fgen0.Get(\"etapi0_moments\")\n", "gen_tree.Print()" ] }, { "cell_type": "markdown", "id": "superior-personality", "metadata": {}, "source": [ "The η and π0 are each reconstructed from their 2γ decays seen in the GlueX detector. The graphs in figure 2 show that after the kinematic fit cut there is a negligible amount of background under both peaks in the 2γ invariant mass spectra. To see the reconstructed mass distributions, we need to access the simulation output events. These data are stored in files named etapi0_moments_x10_n.root, with n=0..9. In the reconstructed tree, several columns are repeated twice, once with an underscore (generated quantities) and a second time without the underscore (reconstructed quantities) so that both types of information is available for all of the reconstructed events." ] }, { "cell_type": "code", "execution_count": 5, "id": "classical-aruba", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Info in : created default TCanvas with name c1\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frec0 = ROOT.TFile(datadir + \"/etapi0_moments_x10_0.root\")\n", "rec_tree = frec0.Get(\"etapi0_moments\")\n", "#rec_tree.Print()\n", "\n", "gen_tree.Draw(\"massEtaPi0\")\n", "h1 = ROOT.gROOT.FindObject(\"htemp\")\n", "h1.GetYaxis().SetTitle(\"events generated (blue), reconstructed (red)\")\n", "h2 = h1.Clone('h2')\n", "h2.SetLineColor(2)\n", "rec_tree.Draw(\"massEtaPi0>>h2\", \"\", \"same\")\n", "c1 = ROOT.gROOT.FindObject(\"c1\")\n", "c1.Draw()" ] }, { "cell_type": "code", "execution_count": 6, "id": "decent-cisco", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gen_tree.Draw(\"abst\")\n", "h1 = ROOT.gROOT.FindObject(\"htemp\")\n", "h1.GetYaxis().SetTitle(\"events generated (blue), reconstructed (red)\")\n", "c1.SetLogy()\n", "h2 = h1.Clone('h2')\n", "h2.SetLineColor(2)\n", "rec_tree.Draw(\"abst>>h2\", \"\", \"same\")\n", "c1.Draw()" ] }, { "cell_type": "markdown", "id": "supposed-aging", "metadata": {}, "source": [ "## Figure 2\n", "The η and π0 are each reconstructed from their 2γ decays seen in the GlueX detector. The graphs in figure 2 show that after the kinematic fit cut there is a negligible amount of background under both peaks in the 2γ invariant mass spectra. To see the reconstructed mass distributions, we need to access the simulation output events. These data are stored in files named etapi0_moments_x10_n.root, with n=0..9." ] }, { "cell_type": "code", "execution_count": 7, "id": "eligible-express", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rec_tree.Draw(\"massEta\")\n", "h1 = ROOT.gROOT.FindObject(\"htemp\")\n", "h1.GetYaxis().SetTitle(\"events\")\n", "c1.SetLogy(0)\n", "c1.Draw()" ] }, { "cell_type": "code", "execution_count": 8, "id": "lyric-multiple", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rec_tree.Draw(\"massPi0\")\n", "h1 = ROOT.gROOT.FindObject(\"htemp\")\n", "h1.GetYaxis().SetTitle(\"events\")\n", "c1.SetLogy(0)\n", "c1.Draw()" ] }, { "cell_type": "markdown", "id": "requested-mexican", "metadata": {}, "source": [ "## Mock data sample\n", "The full set of 27.1 million reconstructed events is divided into two equal parts. The first 13.5 million are used to create a sample of mock data whose decay angular distributions are made to follow a made-up production cross section of the ηπ0 system with angular moments that depend on both mX and |t|. The model has been created to be deliberately complicated and obtuse so that the moments extraction algorithm is not advantaged by simplifiying assumptions. The following plots show the angular distribution of the decay angles θGJGJ converted into moments by taking their inner product with the real spherical harmonics. The model has been constructed to have independent, non-zero moments up to and including L=3, for a total of 49 moments. The moments extraction algorithm does not have the luxury of knowing what this upper limit on the allowed values of L might be, so the goal is to extract all moments up to L=12, for a total of 169 moments." ] }, { "cell_type": "markdown", "id": "proprietary-bradford", "metadata": {}, "source": [ "## Figures 3-4\n", "It is difficult to adequately visualize 49 model moments that vary strongly with both mX and |t| over the full kinematic range shwon in Fig. 1. In figure 3 I select a few moments and plot their dependence on mX for three different bins in |t|. Interested reviewer of these results are encouraged to explore this visualization further by modifying the code below to look at other moments and ranges in |t|." ] }, { "cell_type": "code", "execution_count": 9, "id": "unlimited-theme", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found 10000000 rows in gentree\n" ] } ], "source": [ "# use uproot for faster access when calculations based on tree variables are needed\n", "# if this crashes, restart your kernel with 16 cores, 64GB of ram allocation, or\n", "# else reduce Ngen to limit the number of rows in the tree you load into memory at once.\n", "columns = {\"mom\": \"model1moment\", \"mX\": \"massEtaPi0\", \"|t|\": \"abst\"}\n", "gentree = uproot.open(datadir + \"/generated_moments_x10_0.root:etapi0_moments\")\n", "decomp = ThreadPoolExecutor(16)\n", "Ngen = 10000000\n", "arrays = gentree.arrays(columns.values(), decompression_executor=decomp, entry_start=0, entry_stop=Ngen, array_cache=upcache, library=\"np\")\n", "model1mom = arrays[columns['mom']]\n", "massX = arrays[columns['mX']]\n", "abst = arrays[columns['|t|']]\n", "\n", "moments_to_plot = range(49)\n", "try:\n", " [hgen[i].Reset() for i in range(3)]\n", " [[hmodel[j][i].Reset() for i in range(3)] for j in moments_to_plot]\n", "except:\n", " hgen = [ROOT.TH1D(f\"hgen{i}\", \"\", 50, 0.6, 2.6) for i in range(3)]\n", " hmodel = [[ROOT.TH1D(f\"hmodel{i}{j}\", \"\", 50, 0.6, 2.6) for i in range(3)] for j in moments_to_plot]\n", "print(f\"found {len(massX)} rows in gentree\")\n", "for iev in range(len(massX)):\n", " if abst[iev] > 0.2 and abst[iev] < 0.25:\n", " tbin = 0\n", " elif abst[iev] > 0.9 and abst[iev] < 1.0:\n", " tbin = 1\n", " elif abst[iev] > 1.6 and abst[iev] < 1.8:\n", " tbin = 2\n", " else:\n", " continue\n", " hgen[tbin].Fill(massX[iev])\n", " for j in moments_to_plot:\n", " hmodel[j][tbin].Fill(massX[iev], model1mom[iev][j])" ] }, { "cell_type": "code", "execution_count": 10, "id": "respective-exposure", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning in : Deleting canvas with same name: c1\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h = {}\n", "c = {}\n", "leg = {}\n", "for j in moments_to_plot:\n", " h[j] = [hmodel[j][i].Clone() for i in range(3)]\n", " [h[j][i].Divide(hgen[i]) for i in range(3)]\n", " ymin = min([h[j][i].GetMinimum() for i in range(3)])\n", " ymax = max([h[j][i].GetMaximum() for i in range(3)])\n", " h[j][0].SetStats(0)\n", " h[j][0].SetTitle(f\"model moment {j}\")\n", " h[j][0].GetXaxis().SetTitle(\"mass(#eta#pi^{0}) [GeV]\")\n", " h[j][0].GetYaxis().SetTitle(\"model moment [arb. units])\")\n", " h[j][0].SetMaximum(ymax + (ymax - ymin) * 0.1)\n", " h[j][0].SetMinimum(ymin - (ymax - ymin) * 0.1)\n", " if j < 4:\n", " c[j] = ROOT.TCanvas(f\"c{j}\", f\"c{j}\", 600, 500)\n", " h[j][0].Draw(\"chist\")\n", " canvas = j\n", " elif j == 4:\n", " h[j][0].SetTitle(f\"model moments {j}-{max(moments_to_plot)}\")\n", " h[j][0].SetMaximum(ymax + (ymax - ymin) * 0.2)\n", " h[j][0].SetMinimum(ymin - (ymax - ymin) * 0.2)\n", " c[j] = ROOT.TCanvas(f\"c{j}\", f\"c{j}\", 600, 500)\n", " h[j][0].Draw(\"chist\")\n", " canvas = j\n", " else:\n", " h[j][0].Draw(\"chist same\")\n", " h[j][1].SetLineColor(2)\n", " h[j][1].Draw(\"chist same\")\n", " h[j][2].SetLineColor(4)\n", " h[j][2].Draw(\"chist same\")\n", " leg[f\"leg{j}\"] = ROOT.TLegend(0.6, 0.7, 0.88, 0.88)\n", " leg[f\"leg{j}\"].AddEntry(h[j][0], \"0.2 < |t| < 0.25 GeV^{2}\", 'l')\n", " leg[f\"leg{j}\"].AddEntry(h[j][1], \"0.9 < |t| < 1.0 GeV^{2}\", 'l')\n", " leg[f\"leg{j}\"].AddEntry(h[j][2], \"1.6 < |t| < 1.8 GeV^{2}\", 'l')\n", " leg[f\"leg{j}\"].SetBorderSize(0)\n", " leg[f\"leg{j}\"].Draw()\n", " c[canvas].Draw()\n", " " ] }, { "cell_type": "markdown", "id": "buried-negotiation", "metadata": {}, "source": [ "## Figure 5.\n", "The reconstructed events from the first 50 million generated ηπ0 were used to create a mock data sample by weighting each event according to the model illustrated in figure3, and picking events at random according to their weight by a random process. The plots in figure 4 show the distribution of the mock data sample in generated mX and |t|, demonstrating that the model has shaped the distributions of the reconstructed events in mass and t as well as in the decay anglular distributions." ] }, { "cell_type": "code", "execution_count": 11, "id": "intended-breathing", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning in : Deleting canvas with same name: c1\n", "Warning in : Deleting canvas with same name: c2\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " hdNdmXdt.Delete()\n", "except:\n", " pass\n", "for tbin,mbin in ana.standard_kinematic_bins(finebins=0):\n", " sampledir = datadir + \"/etapi0_moments_{0},{1}_{2},{3}\".format(mbin[0], mbin[1], tbin[0], tbin[1])\n", " fsample = ROOT.TFile(sampledir + \"/Msample.root\")\n", " h2dsample = fsample.Get(\"sample_0\")\n", " f5sample = h5py.File(sampledir + \"/Msample.h5\")\n", " maxweight = f5sample['sample_maxweight'][()]\n", " try:\n", " hdNdmXdt.Add(h2dsample, maxweight)\n", " except:\n", " hdNdmXdt = h2dsample.Clone(\"hdNdmXdt\")\n", " hdNdmXdt.Scale(maxweight)\n", " hdNdmXdt.SetDirectory(0)\n", "hdNdmXdt.SetTitle(\"mock sample #eta,#pi^{0} mass distribution\")\n", "hdNdmXdt.Scale((4 * np.pi)**3)\n", "c1 = ROOT.TCanvas(\"c1\", \"c1\", 700, 500)\n", "hdNdmXdt_x = hdNdmXdt.ProjectionX(\"hdNdmXdt_x\")\n", "hdNdmXdt_x.GetYaxis().SetTitle(\"mock sample events\")\n", "hdNdmXdt_x.SetTitle(\"\")\n", "hdNdmXdt_x.Draw()\n", "c1.Draw()\n", "c2 = ROOT.TCanvas(\"c2\", \"c2\", 700, 500)\n", "c2.cd()\n", "hdNdmXdt_y = hdNdmXdt.ProjectionY(\"hdNdmXdt_y\")\n", "hdNdmXdt_y.GetYaxis().SetTitle(\"mock sample events\")\n", "hdNdmXdt_y.Draw()\n", "hdNdmXdt_y.SetTitle(\"\")\n", "c2.Draw()" ] }, { "cell_type": "markdown", "id": "comfortable-thompson", "metadata": {}, "source": [ "## Figure 6." ] }, { "cell_type": "code", "execution_count": 12, "id": "incomplete-glenn", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hacc_mX = {}\n", "c1.cd()\n", "leg = ROOT.TLegend(0.45, 0.6, 0.88, 0.88)\n", "for tbin,mbin in ana.standard_kinematic_bins(finebins=0):\n", " if tbin == (0.3,0.6):\n", " sampledir = datadir + \"/etapi0_moments_{0},{1}_{2},{3}\".format(mbin[0], mbin[1], tbin[0], tbin[1])\n", " h5 = ana.open(sampledir + \"/Msaved.h5\")\n", " Ngen = h5['generated_subset'][()]\n", " ana.M *= (4 * np.pi)**3 / Ngen\n", " w,v = np.linalg.eigh(ana.M)\n", " name = f\"hacc_mX{mbin}\"\n", " hacc_mX[mbin] = ana.histogram_eigenvalues(name, \"\", w)\n", " plot = len(hacc_mX)\n", " if plot == 1:\n", " hacc_mX[mbin].SetStats(0)\n", " hacc_mX[mbin].SetMaximum(0.6)\n", " hacc_mX[mbin].SetMinimum(0.0)\n", " hacc_mX[mbin].SetLineColor(plot)\n", " hacc_mX[mbin].Draw()\n", " leg.AddEntry(hacc_mX[mbin], f\"t={tbin}GeV^{{2}}, m={mbin}GeV\", 'l')\n", " else:\n", " color = len(hacc_mX)\n", " if color == 5:\n", " color = 42\n", " hacc_mX[mbin].SetLineColor(color)\n", " hacc_mX[mbin].Draw(\"same\")\n", " leg.AddEntry(hacc_mX[mbin], f\"t={tbin}GeV^{{2}}, m={mbin}GeV\", 'l')\n", "leg.SetBorderSize(0)\n", "leg.Draw()\n", "c1.Draw()" ] }, { "cell_type": "code", "execution_count": 13, "id": "manual-credits", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot = 0\n", "hacc_t = {}\n", "c2.cd()\n", "leg2 = ROOT.TLegend(0.48, 0.56, 0.88, 0.88)\n", "for tbin,mbin in ana.standard_kinematic_bins(finebins=2):\n", " if mbin == (1.5,1.6):\n", " sampledir = datadir + \"/etapi0_moments_{0},{1}_{2},{3}\".format(mbin[0], mbin[1], tbin[0], tbin[1])\n", " h5 = ana.open(sampledir + \"/Msaved.h5\")\n", " Ngen = h5['generated_subset'][()]\n", " ana.M *= (4 * np.pi)**3 / Ngen\n", " w,v = np.linalg.eigh(ana.M)\n", " name = f\"hacc_t{tbin}\"\n", " hacc_t[tbin] = ana.histogram_eigenvalues(name, \"\", w)\n", " plot = len(hacc_t)\n", " if plot == 1:\n", " hacc_t[tbin].SetStats(0)\n", " hacc_t[tbin].SetMaximum(0.6)\n", " hacc_t[tbin].SetMinimum(0.0)\n", " hacc_t[tbin].SetLineColor(plot)\n", " hacc_t[tbin].Draw()\n", " leg2.AddEntry(hacc_t[tbin], f\"t={tbin}GeV^{{2}}, m={mbin}GeV\", 'l')\n", " else:\n", " color = len(hacc_t)\n", " if color == 5:\n", " color = 42\n", " hacc_t[tbin].SetLineColor(color)\n", " hacc_t[tbin].Draw(\"same\")\n", " leg2.AddEntry(hacc_t[tbin], f\"t={tbin}GeV^{{2}}, m={mbin}GeV\", 'l')\n", "leg2.SetBorderSize(0)\n", "leg2.Draw()\n", "c2.Draw()" ] }, { "cell_type": "markdown", "id": "eligible-application", "metadata": {}, "source": [ "## Figure 7" ] }, { "cell_type": "code", "execution_count": 14, "id": "bizarre-manhattan", "metadata": {}, "outputs": [], "source": [ "huncor,hcorr,hmodel,hgener = ana.model1_corrected_moments()" ] }, { "cell_type": "code", "execution_count": 15, "id": "cosmetic-ethics", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c1.cd()\n", "hu = huncor[0].ProjectionX()\n", "hu.Draw()\n", "c1.Draw()" ] }, { "cell_type": "code", "execution_count": 16, "id": "extra-phase", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "naive_acceptance = 0.26\n", "offset_for_view_GeV = 0.01\n", "c1.cd()\n", "for imoment in [49]:\n", " hucm = [h.ProjectionX().Rebin(2) for h in (huncor[imoment], hcorr[imoment], hmodel[imoment])]\n", " hucm[0].Scale(1/naive_acceptance)\n", " nxbins = hucm[0].GetNbinsX()\n", " hu_shifted = ROOT.TH1D(hucm[0].GetName() + \"_shifted\", \"\", nxbins, \n", " hucm[0].GetXaxis().GetBinLowEdge(1) + offset_for_view_GeV, \n", " hucm[0].GetXaxis().GetBinUpEdge(nxbins) + offset_for_view_GeV)\n", " ymax,ymin = (0,0)\n", " for j in range(hucm[0].GetNbinsX()):\n", " hu_shifted.SetBinContent(j+1, hucm[0].GetBinContent(j+1))\n", " hu_shifted.SetBinError(j+1, hucm[0].GetBinError(j+1))\n", " for k in range(3):\n", " yval = hucm[k].GetBinContent(j+1)\n", " yerr = hucm[k].GetBinError(j+1)\n", " if yerr < 2e5:\n", " yup = yval + yerr\n", " ydown = yval - yerr\n", " ymax = max((ymax, yup))\n", " ymin = min((ymin, ydown))\n", " hu_shifted.SetTitle(f\"trial sample moment {imoment}\")\n", " hu_shifted.SetLineColor(7)\n", " hu_shifted.SetStats(0)\n", " hu_shifted.SetMaximum(ymax + (ymax - ymin) * 0.1)\n", " hu_shifted.SetMinimum(ymin - (ymax - ymin) * 0.1)\n", " hu_shifted.GetXaxis().SetTitle(\"mass(#eta#pi^{0}) [GeV]\")\n", " hu_shifted.GetYaxis().SetTitle(\"sample moment\")\n", " hu_shifted.Draw()\n", " hucm[1].SetLineColor(4)\n", " hucm[1].Draw(\"same\")\n", " hucm[2].SetLineColor(2)\n", " hucm[2].Draw(\"same\")\n", " c1.Draw()" ] }, { "cell_type": "markdown", "id": "sustainable-context", "metadata": {}, "source": [ "## Figure 8" ] }, { "cell_type": "code", "execution_count": 29, "id": "innovative-compression", "metadata": {}, "outputs": [], "source": [ "hcoarse = [[0,0]] * 5\n", "for tcut in range(1,5):\n", " break # remove this statement to regenerate these figures, takes time!\n", " print(f\"running ana.scan_em on tcut={tcut}\", flush=True)\n", " hcoarse[tcut] = ana.scan_em(finebins=0, tcut=tcut, interactive=0)\n", " try:\n", " [hcoarse[0][i].Add(hcoarse[tcut][i]) for i in range(2)]\n", " except:\n", " hcoarse[0] = [hcoarse[tcut][i].Clone() for i in range(2)]" ] }, { "cell_type": "code", "execution_count": 18, "id": "painted-decrease", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c1.cd()\n", "hcoarse[0][0].Draw()\n", "c1.Draw()" ] }, { "cell_type": "code", "execution_count": 30, "id": "pleased-motion", "metadata": {}, "outputs": [], "source": [ "hfine = [[0,0]] * 9\n", "for tcut in range(1,9):\n", " break # remove this statement to regenerate these figures, takes time!\n", " print(f\"running ana.scan_em on tcut={tcut}\", flush=True)\n", " hfine[tcut] = ana.scan_em(finebins=2, tcut=tcut, interactive=0)\n", " try:\n", " [hfine[0][i].Add(hfine[tcut][i]) for i in range(2)]\n", " except:\n", " hfine[0] = [hfine[tcut][i].Clone() for i in range(2)]" ] }, { "cell_type": "code", "execution_count": 20, "id": "noble-devices", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hfine[0][0].Draw()\n", "c1.Draw()" ] }, { "cell_type": "code", "execution_count": 21, "id": "capable-offset", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CompletedProcess(args=['ps', '-o', 'pid,user,vsz,rss,time,comm,args'], returncode=0)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ " PID USER VSZ RSS TIME COMMAND COMMAND\n", "235590 jonesrt 113292 1640 00:00:00 slurm_script /bin/bash -l /var/spool/sl\n", "235630 jonesrt 1005332 11668 00:00:00 starter-suid Singularity runtime parent\n", "235654 jonesrt 2500 392 00:00:00 tini /opt/conda/bin/tini -g -- \n", "235677 jonesrt 402148 94992 00:00:14 batchspawner-si /opt/conda/bin/python3.8 /\n", "253206 jonesrt 69152700 38205268 01:48:55 python /opt/conda/bin/python -m i\n", "258604 jonesrt 9128 1716 00:00:00 ps ps -o pid,user,vsz,rss,tim\n" ] } ], "source": [ "import subprocess\n", "subprocess.run([\"ps\", \"-o\", \"pid,user,vsz,rss,time,comm,args\"])" ] } ], "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 5 }