From da72c79a61e84fef33568f4e4ddc2efe5f805cc7 Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Fri, 27 Feb 2026 10:17:27 +0100 Subject: [PATCH 01/18] Update to acquire.py help --- acquisition/acquire.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/acquisition/acquire.py b/acquisition/acquire.py index fc40d14..8eeca60 100644 --- a/acquisition/acquire.py +++ b/acquisition/acquire.py @@ -30,13 +30,14 @@ def parse_args(): help="A single string containing \'Background\' or any radioactive source present") parser.add_argument("--source-description", type=str, default='[NOT SPECIFIED]', help="A single string.") - parser.add_argument("--notes", type=str, default='[NONE]') + parser.add_argument("--notes", type=str, default='[NONE]', + help="A single string.") parser.add_argument("--siphra-config-file", type=str, default='D2a/Ongoing.txt', help="By default, it takes the \'Ongoing.txt\' file in the \'D2a\' directory and writes its contents to the metadata file. If any other configuration is used, the path to the text file containing it must be specified here.") parser.add_argument("-s", "--size", type=int, default=4095, - help="([DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS]. Block size. Default is 4095.") + help="[DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS]. Block size. Default is 4095.") parser.add_argument("--device", default="/dev/D2A_DMA", - help = "([DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS].") + help = "[DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS].") return parser.parse_args() From 88913102d4501f4070c322c157cd0f7d2fc42ffc Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Thu, 19 Mar 2026 11:44:05 +0100 Subject: [PATCH 02/18] Added voltage dependency notebook --- noteboks/Voltage_dependency.ipynb | 695 ++++++++++++++++++++++++++++++ 1 file changed, 695 insertions(+) create mode 100644 noteboks/Voltage_dependency.ipynb diff --git a/noteboks/Voltage_dependency.ipynb b/noteboks/Voltage_dependency.ipynb new file mode 100644 index 0000000..b7a1204 --- /dev/null +++ b/noteboks/Voltage_dependency.ipynb @@ -0,0 +1,695 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "248d5111-9526-4226-b6ef-a67dc0c71445", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c9421d1e-23fe-4585-905c-2f56603eafca", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "Cs237_MEV = 0.662" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1ebd8ed-1b60-42dd-b3d9-2372f7596aad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16']\n" + ] + } + ], + "source": [ + "#Make sure json files have matching names to the dat files. \n", + "\n", + "hannels = [f'Ch{n}' for n in range(2,17,2)]\n", + "print(channels)\n", + "files_lsts = {}\n", + "files_lsts[channels[0]] = sorted(Path(project_root+'/data/260311').glob('SUBTRACTED*Ch2*.csv'))\n", + "files_lsts[channels[0]].pop(3)\n", + "files_lsts[channels[0]].pop(3)\n", + "files_lsts[channels[0]].pop(3)\n", + "for ch in channels[1:]:\n", + " files_lsts[ch] = sorted(Path(project_root+'/data/260311').glob(f'SUBTRACTED*{ch}*.csv'))\n", + "\n", + "vbiases = [28.0, 28.5, 29.0]\n", + "# Datasets\n", + "datasets = {}\n", + "for ch, lst in files_lsts.items():\n", + " datasets[ch] = []\n", + " datasets[ch].append(SiphraAcquisition(lst[2]))\n", + " datasets[ch].append(SiphraAcquisition(lst[0]))\n", + " datasets[ch].append(SiphraAcquisition(lst[1]))\n", + "\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kCyan, ROOT.kYellow, ROOT.kSpring, ROOT.kViolet]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4f82a8be-e799-4405-9464-bb085d20cdf3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " #legends_raw[-1].Draw()\n", + " canvas_raw[-1].Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1931d8ae-6b41-4b2d-85cf-33da23d47698", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ae48ab17fd6175c42c3dce1c9d2cae306c6ab33b Mon Sep 17 00:00:00 2001 From: JohannaBark <160016208+JohannaBark@users.noreply.github.com> Date: Thu, 19 Mar 2026 13:44:34 +0100 Subject: [PATCH 03/18] fixed typo --- noteboks/Voltage_dependency.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/noteboks/Voltage_dependency.ipynb b/noteboks/Voltage_dependency.ipynb index b7a1204..a5290db 100644 --- a/noteboks/Voltage_dependency.ipynb +++ b/noteboks/Voltage_dependency.ipynb @@ -53,7 +53,7 @@ "source": [ "#Make sure json files have matching names to the dat files. \n", "\n", - "hannels = [f'Ch{n}' for n in range(2,17,2)]\n", + "channels = [f'Ch{n}' for n in range(2,17,2)]\n", "print(channels)\n", "files_lsts = {}\n", "files_lsts[channels[0]] = sorted(Path(project_root+'/data/260311').glob('SUBTRACTED*Ch2*.csv'))\n", From bccf791e5ba30ca1c8d73546706c3652d2f33b34 Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Tue, 24 Mar 2026 14:51:25 +0100 Subject: [PATCH 04/18] energy resolution notebook created --- noteboks/energyResolution.ipynb | 308 ++++++++ noteboks/gainTesting.ipynb | 687 ++++++++++++++++++ .../__pycache__/metadata.cpython-314.pyc | Bin 2752 -> 2877 bytes .../siphraacquisition.cpython-314.pyc | Bin 12400 -> 12400 bytes processing/metadata.py | 3 + 5 files changed, 998 insertions(+) create mode 100644 noteboks/energyResolution.ipynb create mode 100644 noteboks/gainTesting.ipynb diff --git a/noteboks/energyResolution.ipynb b/noteboks/energyResolution.ipynb new file mode 100644 index 0000000..9a424b1 --- /dev/null +++ b/noteboks/energyResolution.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4a3b1bc5-ec73-4d69-a840-07d057f4e2eb", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition, MetadataLoader\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "75493f51-2089-4f0a-8057-0f2c2bf7466f", + "metadata": {}, + "outputs": [], + "source": [ + "files = sorted((project_root/'data/260323').glob('SUBT_*'))\n", + "acqs = [SiphraAcquisition(file) for file in files]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5ee51f11-cd24-44c1-bfe5-f0e3b7d8a85c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SIPHRAAcquisition(File: 'SUBT_01_EnergyResolution_thr30_gain1over100_1over3_Background'),\n", + " SIPHRAAcquisition(File: 'SUBT_02_EnergyResolution_thr30_gain1over100_1over3_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'),\n", + " SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22'),\n", + " SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'),\n", + " SIPHRAAcquisition(File: 'SUBT_06_EnergyResolution_thr30_gain1over100_1over3_Co60'),\n", + " SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'),\n", + " SIPHRAAcquisition(File: 'SUBT_08_EnergyResolution_thr30_gain1over100_1over3_Am241'),\n", + " SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "88f4c856-78c0-4ca6-ad58-cc10f3b53649", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cs-137\n", + "Na-22\n", + "Co-60\n", + "Am-241\n", + "{'Cs-137': , 'Cs-137_BG': , 'Cs-137_clean': , 'Na-22': , 'Na-22_BG': , 'Na-22_clean': , 'Co-60': , 'Co-60_BG': , 'Co-60_clean': , 'Am-241': , 'Am-241_BG': , 'Am-241_clean': }\n" + ] + } + ], + "source": [ + "# histograms\n", + "hists = {}\n", + "sources = []\n", + "for sgnl, bg in zip(acqs[1::2], acqs[2::2]):\n", + " filepath = sgnl.filepath.stem\n", + " src = (MetadataLoader.load(sgnl.metadataFile)).source\n", + " sources.append(src)\n", + " print(src)\n", + " # Signal and Background\n", + " hist_sgnl = ROOT.TH1F(f\"{src} Signal\", \"\", BITS12, 0, BITS12)\n", + " hist_bg = ROOT.TH1F(f\"{src} Background\", \"\", BITS12, 0 , BITS12)\n", + " hist_sgnl.Fill(sgnl['s']/len(sgnl.active_chs))\n", + " hist_bg.Fill(bg['s']/len(bg.active_chs))\n", + " hist_bg.Scale(sgnl.exposure/bg.exposure)\n", + " # Clean spectrum\n", + " hist_clean = hist_sgnl.Clone(f\"{src} Clean\")\n", + " hist_clean.Add(hist_bg, -1)\n", + " for hist in [hist_sgnl, hist_bg, hist_clean]:\n", + " # hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Normalized counts\")\n", + " hists[src] = hist_sgnl\n", + " hists[f\"{src}_BG\"] = hist_bg\n", + " hists[f\"{src}_clean\"] = hist_clean\n", + " del hist_sgnl, hist_bg\n", + "print(hists)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "36f654a6-bd64-471f-a0e3-01927db52e3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", + "cv.Divide(2,2)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", + " cv.cd(idx+1)\n", + " \n", + " sgnl = hists[src]\n", + " bg = hists[src+'_BG']\n", + " clean = hists[src+'_clean']\n", + " \n", + " lg.AddEntry(sgnl, \"Signal\")\n", + " lg.AddEntry(bg, \"Background\")\n", + " lg.AddEntry(clean, \"Subtracted\")\n", + " sgnl.SetLineColor(colors[0])\n", + " bg.SetLineColor(colors[1])\n", + " clean.SetLineColor(colors[2])\n", + " sgnl.SetTitle(src)\n", + " sgnl.Draw(\"hist\")\n", + " bg.Draw(\"sames hist\")\n", + " clean.Draw(\"sames hist\")\n", + " ROOT.gPad.Update()\n", + " for i, sp in enumerate([sgnl, bg, clean]):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " lg.Draw()\n", + " cv.cd(idx+1).SetLogy()\n", + " cv.Draw()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "6db7ba78-ddd9-4d76-8bba-bf310f8f1221", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sources\n", + "hists[sources[1]+'_BG']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6b5a5060-fe8f-48ba-af47-c12bca5ea672", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'),\n", + " SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'),\n", + " SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'),\n", + " SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs[2::2]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/noteboks/gainTesting.ipynb b/noteboks/gainTesting.ipynb new file mode 100644 index 0000000..7aa8f5b --- /dev/null +++ b/noteboks/gainTesting.ipynb @@ -0,0 +1,687 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 66, + "id": "cc81fa7c-d43e-4c17-854d-8bc51e7bdf91", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "f14c60fa-8e5a-4c1b-bd05-6d8f0a23851d", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "9d972c1c-3934-4a10-9e7c-4e7b9caa4b3f", + "metadata": {}, + "outputs": [], + "source": [ + "files1 = sorted((project_root/'data/260317').glob('1*.csv'))\n", + "acqs1 = [SiphraAcquisition(file) for file in files1]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "2696fa6e-8416-4df7-9bf9-62d00adcee3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SIPHRAAcquisition(File: '10_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '11_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '12_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '15_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '16_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '17_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '18_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1')]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs1.pop(3)\n", + "acqs1.pop(3)\n", + "acqs1" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "af88b5d2-3d88-4310-ad81-90986efb6ffd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'10'" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs1[0].filepath.stem[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "8d9cd696-eb5e-46c8-9673-73e2afc2ebc5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: cmis_gain 1/10 (Potential memory leak).\n", + "Warning in : Replacing existing TH1: cmis_gain 1/100 (Potential memory leak).\n", + "Warning in : Replacing existing TH1: cmis_gain 1/200 (Potential memory leak).\n", + "Warning in : Replacing existing TH1: cmis_gain 1/400 (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "lg.SetHeader('CMIS gain')\n", + "\n", + "ci_gain='1V/0.75nC'\n", + "cmis_gains=['1/10', '1/100', '1/200', '1/400']\n", + "hists={}\n", + "\n", + "for idx, (acq, cmsg) in enumerate(zip(acqs1[:4], cmis_gains)):\n", + " hist = ROOT.TH1F(f\"cmis_gain {cmsg}\", f\"{cmsg}\", BITS12, 0, BITS12)\n", + " hist.Fill(acq['s']/len(acq.active_chs))\n", + " hist.Scale(1/acq.exposure)\n", + " # Preeting up ...\n", + " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist.SetLineColor(colors[idx]+2)\n", + " # legend and storing\n", + " lg.AddEntry(hist, f\"{cmsg}\")\n", + " hists[f\"Acq{acq.filepath.stem[:2]}\"] = hist\n", + " del hist\n", + " hists[f\"Acq{acq.filepath.stem[:2]}\"].Draw('sames hist')\n", + "cv.SetLogy()\n", + "lg.Draw()\n", + "cv.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "65e8172d-a2bc-4306-87b7-66027afc15f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibration with acquisition 15(Signal) and 16 (BG)\n", + "files2 = sorted([p for p in (project_root/'data/260317').iterdir() if p.stem[11:13] in ['15', '16']])\n", + "acqs2 = [SiphraAcquisition(file) for file in files2]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "b831a930-27c1-442f-8dd0-e30f2d8a84a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260317/SUBTRACTED_15_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1.csv'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260317/SUBTRACTED_16_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1.csv')]" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files2" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0fcebaeb-0ace-4f57-b151-3a80a51535e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(acqs2[0].active_chs)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "f6a83f88-b203-4af0-9f0b-18a7b5a0338f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposures:\n", + " Signal:\t220.216 s\n", + " Background:\t625.392 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: Signal (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Background (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "# lg.SetHeader('')\n", + "\n", + "ci_gain='1V/0.75nC'\n", + "labels=['Signal', 'Background']\n", + "\n", + "ref_exposure = acqs2[0].exposure\n", + "\n", + "for idx, (acq, label) in enumerate(zip(acqs2, labels)):\n", + " hist = ROOT.TH1F(label, label, BITS12, 0, BITS12)\n", + " hist.Fill(acq['s']/len(acq.active_chs))\n", + " # hist.Scale(ref_exposure/acq.exposure)\n", + " # Preeting up ...\n", + " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC, CMIS gain = 1/400\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Counts\")\n", + " hist.SetLineColor(colors[idx]+2)\n", + " # legend and storing\n", + " lg.AddEntry(hist, label)\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"] = hist\n", + " del hist\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"].Draw('sames hist')\n", + "\n", + "# hist_subt = hists['AcqSUBTRACTED_15'].Clone('Subtracted')\n", + "# hist_subt.Add(hists['AcqSUBTRACTED_16'], -1)\n", + "# hist_subt.Draw('sames hist')\n", + "# hist_subt.SetLineColor(colors[2])\n", + "# lg.AddEntry(hist_subt, 'Subtracted')\n", + "\n", + "cv.SetLogy()\n", + "lg.Draw()\n", + "cv.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "print(f\"Exposures:\\n Signal:\\t{acqs2[0].exposure:>.3f} s\\n Background:\\t{acqs2[1].exposure:>.3f} s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "05919cfa-b075-4898-8f7b-56039d4c8da1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Acq10': ,\n", + " 'Acq11': ,\n", + " 'Acq12': ,\n", + " 'Acq15': ,\n", + " 'AcqSUBTRACTED_10': ,\n", + " 'AcqSUBTRACTED_11': ,\n", + " 'AcqSUBTRACTED_16': ,\n", + " 'AcqSUBTRACTED_15': }" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hists" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "8e7a42e8-c3a4-451c-b635-5d37c1167faf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 7605.91\n", + "Chi2 = 15211.8\n", + "NDf = 197\n", + "Edm = 1.10191e-06\n", + "NCalls = 138\n", + "Constant = 1267.88 +/- 5.7826 \n", + "Mean = 172.076 +/- 0.0854468 \n", + "Sigma = 22.9615 +/- 0.0616869 \t (limited)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "files2 = sorted((project_root/'data/260317').glob('SUBTRACTED_*'))\n", + "acqs2 = [SiphraAcquisition(file) for file in files2]\n", + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "\n", + "hist = hist_subt.Clone('hist_subt_calib')\n", + "hist.SetTitle(r'^{22}Na subtracted spectrum, CI gain = 1/0.75 pC, CMIS gain = 1/400')\n", + "fit1 = hist.Fit('gaus', 'L S', \"\", 100, 300)\n", + "hist.Draw()\n", + "cv.SetLogy()\n", + "cv.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "daf8a1de-c392-4879-a4a9-619db60f669e", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibration with acquisition 15(Signal) and 16 (BG)\n", + "files2 = sorted((project_root/'data/260317').glob('SUBTRACTED_*'))\n", + "acqs2 = [SiphraAcquisition(file) for file in files2]\n", + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "# lg.SetHeader('CMIS gain')\n", + "\n", + "ci_gain='1V/0.75nC'\n", + "labels=['Signal', 'Background']\n", + "hists={}\n", + "\n", + "for idx, (acq, label) in enumerate(zip(acqs2, labels)):\n", + " hist = ROOT.TH1F(label, label, BITS12, 0, BITS12)\n", + " hist.Fill(acq['s']/len(acq.active_chs))\n", + " hist.Scale(1/acq.exposure)\n", + " # Preeting up ...\n", + " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC, CMIS gain = 1/400\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist.SetLineColor(colors[idx]+2)\n", + " # legend and storing\n", + " lg.AddEntry(hist, label)\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"] = hist\n", + " del hist\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"].Draw('sames hist')\n", + "\n", + "\n", + "\n", + "cv.SetLogy()\n", + "lg.Draw()\n", + "cv.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "2028d39f-0e60-4bbe-b3c9-55cbbc63b14b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2004" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2278 - 274" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "f3576c1a-50d1-4778-920f-1dd754dd473d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1204" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1841 - 637" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "a74af979-2cdc-4da3-8164-59ab0a3be28d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "560" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "984-424" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/processing/__pycache__/metadata.cpython-314.pyc b/processing/__pycache__/metadata.cpython-314.pyc index 7e1887bc5c9c0cb95b617e0f7585c9a46e6ab4bf..d26f49e765a4ea101e38cddc11e3315eb9d2e7ef 100644 GIT binary patch delta 957 zcmZ`%&1(}u6rb7MY&M%@n>J};v`MyYj14xmTEv4zTLcl&9#RZ~#kwXd4YW-jxM7((PlxaB95wgMy=T1!8WyNZj+caX*F$XNHw%EQWY;n6Axa53W4TSl$aE<(3&{A@nD4p zDb%AfHxCv)co&NoQF`lCA#a2-8y-k!{9N5pknR(yuynQ>b$95CetTkc+ zj`jV=_Dkitb(*m|@*-PTvyj*12X;or`8K;GcX)|^56OUXZ#ItU4nveg0x68Ypuh5`8hV^buUz_-|E}W5qE7IeB5nt+{r6ZN+W28lph! zMZy#zLzqSkU!g255=Lo*6nKTO7+EzZ;`oR56wqToI7TAnr^-z;SDwSr3$$x)bJe~f zs5TKJ=mh^(`o2_ZUq;u-GZxlbjn3-I1yPn?&G~7S#d?6iGkB=SF**n*nYjlDU-k!V C_LNEh diff --git a/processing/__pycache__/siphraacquisition.cpython-314.pyc b/processing/__pycache__/siphraacquisition.cpython-314.pyc index 54d32b42c7bc5a68819b3116256566cba19110b3..ef29366677ba00b21fa1df749d1407dd9b0a3ec5 100644 GIT binary patch delta 20 acmey6@F9U)n~#@^0SNA#*u9ZE)c^oUhz6_x delta 20 acmey6@F9U)n~#@^0SKlyZQRJ6Y5)L4&juF& diff --git a/processing/metadata.py b/processing/metadata.py index 84c3501..8321d42 100644 --- a/processing/metadata.py +++ b/processing/metadata.py @@ -11,6 +11,7 @@ class Metadata: active_chs: list[int] sipm_chs: str | list[str] n_events: int + source: str | None class MetadataLoader: @@ -29,10 +30,12 @@ def load(path: PathLike) -> Metadata: @staticmethod def _parse_v1(raw: dict) -> Metadata: acq = raw["acquisition"] + source = raw["source"]["type"] return Metadata( exposure_sec=acq["exposure_sec"], active_chs=[int(ch) for ch in acq["active_chs"]], sipm_chs=acq["sipm_chs"], n_events=acq["counts"], + source=source, ) \ No newline at end of file From 35c4b7f3282a86c7a2795ae3ee2aa3ee632bb99c Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Thu, 26 Mar 2026 16:39:02 +0100 Subject: [PATCH 05/18] Calibration --- noteboks/energyResolution-Copy1.ipynb | 539 ++++++++++++++++++++++++++ 1 file changed, 539 insertions(+) create mode 100644 noteboks/energyResolution-Copy1.ipynb diff --git a/noteboks/energyResolution-Copy1.ipynb b/noteboks/energyResolution-Copy1.ipynb new file mode 100644 index 0000000..f91fc22 --- /dev/null +++ b/noteboks/energyResolution-Copy1.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4a3b1bc5-ec73-4d69-a840-07d057f4e2eb", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition, MetadataLoader\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "\n", + "# Energy emission peaks in MeV\n", + "Cs137_MeV = 0.662\n", + "Na22_MeV = [0.511, 1.275, 1.786]\n", + "Co60_MeV = [1.173, 1.332, 2,505]\n", + "Am241_MeV = 0.0595\n", + "# Background emission\n", + "K40_MeV = 1.460\n", + "Tl208_MEV = 2.614\n", + "\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75493f51-2089-4f0a-8057-0f2c2bf7466f", + "metadata": {}, + "outputs": [], + "source": [ + "files = sorted((project_root/'data/260323').glob('SUBT_*'))\n", + "acqs = [SiphraAcquisition(file) for file in files]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "88f4c856-78c0-4ca6-ad58-cc10f3b53649", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cs-137\n", + "Na22\n", + "Co-60\n", + "Am-241\n" + ] + } + ], + "source": [ + "# histograms\n", + "hists = {}\n", + "sources = []\n", + "for sgnl, bg in zip(acqs[1::2], acqs[2::2]):\n", + " filepath = sgnl.filepath.stem\n", + " src = (MetadataLoader.load(sgnl.metadataFile)).source\n", + " sources.append(src)\n", + " print(src)\n", + " # Signal and Background\n", + " hist_sgnl = ROOT.TH1F(f\"{src} Signal\", \"\", BITS12, 0, BITS12)\n", + " hist_bg = ROOT.TH1F(f\"{src} Background\", \"\", BITS12, 0 , BITS12)\n", + " hist_sgnl.Fill(sgnl['s']/len(sgnl.active_chs))\n", + " hist_bg.Fill(bg['s']/len(bg.active_chs))\n", + " hist_bg.Scale(sgnl.exposure/bg.exposure)\n", + " # Clean spectrum\n", + " hist_clean = hist_sgnl.Clone(f\"{src} Clean\")\n", + " hist_clean.Add(hist_bg, -1)\n", + " for hist in [hist_sgnl, hist_bg, hist_clean]:\n", + " # hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Normalized counts\")\n", + " hists[src] = hist_sgnl\n", + " hists[f\"{src}_BG\"] = hist_bg\n", + " hists[f\"{src}_clean\"] = hist_clean\n", + " del hist_sgnl, hist_bg\n", + "#print(hists)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "36f654a6-bd64-471f-a0e3-01927db52e3e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", + "cv.Divide(2,2)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", + " cv.cd(idx+1)\n", + " \n", + " sgnl = hists[src]\n", + " bg = hists[src+'_BG']\n", + " clean = hists[src+'_clean']\n", + " \n", + " lg.AddEntry(sgnl, \"Signal\")\n", + " lg.AddEntry(bg, \"Background\")\n", + " lg.AddEntry(clean, \"Subtracted\")\n", + " sgnl.SetLineColor(colors[0])\n", + " bg.SetLineColor(colors[1])\n", + " clean.SetLineColor(colors[2])\n", + " sgnl.SetTitle(src)\n", + " sgnl.Draw(\"hist\")\n", + " bg.Draw(\"sames hist\")\n", + " clean.Draw(\"sames hist\")\n", + " ROOT.gPad.Update()\n", + " for i, sp in enumerate([sgnl, bg, clean]):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " lg.Draw()\n", + " cv.cd(idx+1).SetLogy()\n", + " cv.Draw()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6db7ba78-ddd9-4d76-8bba-bf310f8f1221", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.004280158214429103\n", + "-0.2655932438428593\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 196.518\n", + "NDf = 127\n", + "Edm = 2.29967e-07\n", + "NCalls = 65\n", + "Constant = 2459.66 +/- 7.74946 \n", + "Mean = 183.993 +/- 0.078652 \n", + "Sigma = 29.2438 +/- 0.0713573 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 356.977\n", + "NDf = 147\n", + "Edm = 1.20533e-06\n", + "NCalls = 64\n", + "Constant = 1361.97 +/- 6.38965 \n", + "Mean = 275.54 +/- 0.144272 \n", + "Sigma = 32.7382 +/- 0.141925 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 52.0578\n", + "NDf = 47\n", + "Edm = 1.53695e-06\n", + "NCalls = 107\n", + "Constant = 255.901 +/- 3.65957 \n", + "Mean = 478.856 +/- 0.861944 \n", + "Sigma = 32.2107 +/- 1.8725 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 50.431\n", + "NDf = 47\n", + "Edm = 6.26293e-06\n", + "NCalls = 110\n", + "Constant = 187.956 +/- 4.04943 \n", + "Mean = 177.384 +/- 1.67095 \n", + "Sigma = 37.5292 +/- 4.35406 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 152.171\n", + "NDf = 97\n", + "Edm = 2.80563e-08\n", + "NCalls = 96\n", + "Constant = 606.675 +/- 4.80363 \n", + "Mean = 371.93 +/- 0.452134 \n", + "Sigma = 45.3973 +/- 0.782852 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 63.6754\n", + "NDf = 67\n", + "Edm = 1.95162e-06\n", + "NCalls = 89\n", + "Constant = 880.588 +/- 5.38234 \n", + "Mean = 471.391 +/- 0.435139 \n", + "Sigma = 32.0034 +/- 0.470993 \t (limited)\n", + "****************************************\n", + "Minimizer is Linear / Migrad\n", + "Chi2 = 0.00408946\n", + "NDf = 1\n", + "p0 = -0.265593 +/- 0.109303 \n", + "p1 = 0.00428016 +/- 0.000302376 \n" + ] + } + ], + "source": [ + "#Fitting all the visible peaks. Values around peak input manually\n", + "\n", + "Cs137_fit = ROOT.TF1(\"Cs137_fit\", \"gaus\", 120,250)\n", + "\n", + "Co60a_fit = ROOT.TF1(\"Co60a_fit\", \"gaus\", 200,350)\n", + "Co60b_fit = ROOT.TF1(\"Co60b_fit\", \"gaus\", 450,500)\n", + "\n", + "Na22a_fit = ROOT.TF1(\"Na22a_fit\", \"gaus\", 150,200)\n", + "Na22b_fit = ROOT.TF1(\"Na22b_fit\", \"gaus\", 320,420)\n", + "Na22c_fit = ROOT.TF1(\"Na22c_fit\", \"gaus\", 450,520)\n", + "\n", + "fits = [Cs137_fit, Co60a_fit, Co60b_fit, Na22a_fit, Na22b_fit, Na22c_fit]\n", + "\n", + "#Fit to histogram and find mean value of peak\n", + "hists['Cs-137_clean'].Fit(Cs137_fit, \"R+S\")\n", + "hists['Co-60_clean'].Fit(Co60a_fit, \"R+S\")\n", + "hists['Co-60_clean'].Fit(Co60b_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22a_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22b_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22c_fit, \"R+S\")\n", + "\n", + "Cs137_mean = Cs137_fit.GetParameter('Mean')\n", + "Co60a_mean = Co60a_fit.GetParameter('Mean')\n", + "Co60b_mean = Co60b_fit.GetParameter('Mean')\n", + "Na22a_mean = Na22a_fit.GetParameter('Mean')\n", + "Na22b_mean = Na22b_fit.GetParameter('Mean')\n", + "Na22c_mean = Na22c_fit.GetParameter('Mean')\n", + "\n", + "channels = [Na22a_mean, Na22b_mean, Na22c_mean]\n", + "energies = Na22_MeV\n", + "channels = np.array(channels, dtype='float64')\n", + "energies = np.array(energies, dtype='float64')\n", + "\n", + "g = ROOT.TGraph(len(channels), channels, energies)\n", + "\n", + "f = ROOT.TF1(\"calib\", \"pol1\", min(channels), max(channels))\n", + "g.Fit(f)\n", + "\n", + "a = f.GetParameter(1)\n", + "b = f.GetParameter(0)\n", + "print(a)\n", + "print(b)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "\n", + "#lg.Draw()\n", + "#cv.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6b5a5060-fe8f-48ba-af47-c12bca5ea672", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 137.647\n", + "NDf = 67\n", + "Edm = 2.07898e-06\n", + "NCalls = 84\n", + "Constant = 189.854 +/- 2.40817 \n", + "Mean = 0.492681 +/- 0.00341376 \n", + "Sigma = 0.142338 +/- 0.00437932 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 155.228\n", + "NDf = 91\n", + "Edm = 3.6702e-08\n", + "NCalls = 96\n", + "Constant = 613.942 +/- 3.98488 \n", + "Mean = 1.32007 +/- 0.00148807 \n", + "Sigma = 0.180551 +/- 0.00242551 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 372.407\n", + "NDf = 90\n", + "Edm = 6.00943e-09\n", + "NCalls = 92\n", + "Constant = 833.135 +/- 4.70932 \n", + "Mean = 1.74383 +/- 0.00131477 \n", + "Sigma = 0.172367 +/- 0.0019528 \t (limited)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv_cal'):\n", + " ROOT.gROOT.FindObject('cv_cal').Close()\n", + "\n", + " \n", + "hist = hists['Na22_clean']\n", + "\n", + "emin = a * hist.GetXaxis().GetXmin() + b\n", + "emax = a * hist.GetXaxis().GetXmax() + b\n", + "\n", + "h_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\",\n", + " hist.GetNbinsX(), emin, emax)\n", + "\n", + "for i in range(1, hist.GetNbinsX() + 1):\n", + " counts = hist.GetBinContent(i)\n", + " h_cal.SetBinContent(i, counts)\n", + "\n", + "h_cal.GetXaxis().SetTitle(\"Energy (MeV)\")\n", + "\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv_cal = ROOT.TCanvas('cv_cal', 'cv_cal', 1600, 1200)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "\n", + "\n", + "Na22a_fit_cal = ROOT.TF1(\"Na22a_fit_cal\", \"gaus\", 0.3, 0.6)\n", + "Na22b_fit_cal = ROOT.TF1(\"Na22b_fit_cal\", \"gaus\", 1.1 , 1.5)\n", + "Na22c_fit_cal = ROOT.TF1(\"Na22c_fit_cal\", \"gaus\", 1.5, 1.9)\n", + "h_cal.Fit(Na22a_fit_cal, \"R+S\")\n", + "h_cal.Fit(Na22b_fit_cal, \"R+S\")\n", + "h_cal.Fit(Na22c_fit_cal, \"R+S\")\n", + "\n", + "h_cal.Draw(\"hist\")\n", + "cv_cal.cd(idx+1).SetLogy()\n", + "\n", + "cv_cal.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8947e352-94db-4b13-8666-c1f2d7eca282", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 68257d5e52f883b8b5fd97dd0a27cb6e5826b8db Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Fri, 27 Mar 2026 10:33:31 +0100 Subject: [PATCH 06/18] update --- .../22Na_60Co_bg-subtraction_calib.ipynb | 261 ++++ notebooks/260204_CC_BG_comparison.ipynb | 296 +++++ notebooks/260205.ipynb | 288 +++++ notebooks/260206_CMIS_voffset.ipynb | 154 +++ notebooks/260209_Cs137_BGsubtraction.ipynb | 253 ++++ notebooks/260212.ipynb | 214 +++ notebooks/CT_Cs137_calib.ipynb | 731 +++++++++++ notebooks/QT_compare_channels.ipynb | 532 ++++++++ notebooks/Untitled.ipynb | 229 ++++ notebooks/Voltage_dependency.ipynb | 695 ++++++++++ notebooks/banana.ipynb | 464 +++++++ notebooks/baseline_subtraction.ipynb | 1143 +++++++++++++++++ notebooks/energyResolution.ipynb | 307 +++++ notebooks/etcTests/converters_explore.ipynb | 313 +++++ notebooks/etcTests/siphraAcq_test_json.ipynb | 125 ++ notebooks/gainTesting.ipynb | 687 ++++++++++ notebooks/histogram_summed.ipynb | 294 +++++ notebooks/img/etc/CMIS_internal.png | Bin 0 -> 54280 bytes notebooks/img/etc/SIPHRA_arch.png | Bin 0 -> 905852 bytes notebooks/include_project_root.py | 1 + notebooks/spectra_current_thrsh.ipynb | 529 ++++++++ notebooks/spectrum_plotter.ipynb | 1022 +++++++++++++++ .../ComptonCam_DMA_To_RAW_File/Makefile | 17 + .../src/dma_to_raw_file.c | 437 +++++++ siphractrl/MK_WriteConfiguration_251210.py | 30 + .../__pycache__/d2a_lib.cpython-314.pyc | Bin 0 -> 13671 bytes siphractrl/acquire.py | 107 ++ siphractrl/d2a_lib.py | 320 +++++ siphractrl/regs_bit_structure.py | 104 ++ siphractrl/siphra_controller.py | 112 ++ 30 files changed, 9665 insertions(+) create mode 100644 notebooks/22Na_60Co_bg-subtraction_calib.ipynb create mode 100644 notebooks/260204_CC_BG_comparison.ipynb create mode 100644 notebooks/260205.ipynb create mode 100644 notebooks/260206_CMIS_voffset.ipynb create mode 100644 notebooks/260209_Cs137_BGsubtraction.ipynb create mode 100644 notebooks/260212.ipynb create mode 100644 notebooks/CT_Cs137_calib.ipynb create mode 100644 notebooks/QT_compare_channels.ipynb create mode 100644 notebooks/Untitled.ipynb create mode 100644 notebooks/Voltage_dependency.ipynb create mode 100644 notebooks/banana.ipynb create mode 100644 notebooks/baseline_subtraction.ipynb create mode 100644 notebooks/energyResolution.ipynb create mode 100644 notebooks/etcTests/converters_explore.ipynb create mode 100644 notebooks/etcTests/siphraAcq_test_json.ipynb create mode 100644 notebooks/gainTesting.ipynb create mode 100644 notebooks/histogram_summed.ipynb create mode 100644 notebooks/img/etc/CMIS_internal.png create mode 100644 notebooks/img/etc/SIPHRA_arch.png create mode 100644 notebooks/include_project_root.py create mode 100644 notebooks/spectra_current_thrsh.ipynb create mode 100644 notebooks/spectrum_plotter.ipynb create mode 100644 siphractrl/ComptonCam_DMA_To_RAW_File/Makefile create mode 100644 siphractrl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c create mode 100644 siphractrl/MK_WriteConfiguration_251210.py create mode 100644 siphractrl/__pycache__/d2a_lib.cpython-314.pyc create mode 100644 siphractrl/acquire.py create mode 100644 siphractrl/d2a_lib.py create mode 100644 siphractrl/regs_bit_structure.py create mode 100644 siphractrl/siphra_controller.py diff --git a/notebooks/22Na_60Co_bg-subtraction_calib.ipynb b/notebooks/22Na_60Co_bg-subtraction_calib.ipynb new file mode 100644 index 0000000..a84fdbc --- /dev/null +++ b/notebooks/22Na_60Co_bg-subtraction_calib.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cda6a9c1-c4c0-4e69-b50d-7dcb1b8434f2", + "metadata": {}, + "source": [ + "# Calibrating using 22-Na and 60-Co\n", + "\n", + "Baseline-subtracted acquisitions were used to determine if 22-Na and 60-Co were siutable peaks for energy calibration. We also included a 137-Cs source in the acquisition, with the intention of using the peak to check the accuracy of the calibration. \n", + "\n", + "The baseline-subtracted histograms of an acquisition with 2 active channels were obtained. The baseline value for each channel is the mean of the readings from all the events triggered ONLY by external HOLD assertion. This value is subtracted from the values of each \"real\" event (triggered from internal HOLD).\n", + "\n", + "The dataset for this analysis is a background acquisition of 500 000 events. SIPHRA channels 2 and 14 were connected to SiPM channels 1-4 and 5-8, respectively, using charge comparator, with a threshold value of 20. The source acquisition registered 1 000 000 events. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:32.276396Z", + "start_time": "2026-02-10T09:37:32.169808Z" + } + }, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a42cc03d-bbcb-43b2-80ef-d06eaf81ab7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.662\n", + "NA22_MEV = 0.511\n", + "CO60_MEV_1 = 1.173\n", + "CO60_MEV_2 = 1.332\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68a9cd08dc972f65", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:35.457562Z", + "start_time": "2026-02-10T09:37:33.669215Z" + } + }, + "outputs": [], + "source": [ + "# Datasets\n", + "acq_background = SiphraAcquisition(project_root+'/data/260223/5_SiPM_Ch1-4_Ch2_Ch5-8_Ch14_Active_1-16_PT100_background.csv',\n", + " active_chs=[2,14],\n", + " exposure_sec=277.478)\n", + "acq_sources = SiphraAcquisition(project_root+'/data/260223/3_SiPM_Ch1-4_Ch2_Ch5-8_Ch14_Active_1-16_PT100_Sources_Cs137_Co60_Na22.csv',\n", + " active_chs=[2,14],\n", + " exposure_sec=448.742)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cae21a3ee684d69f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:43:25.401070Z", + "start_time": "2026-02-10T09:43:24.995723Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "SUMMED_XR=5000 # Right limit of the summed channel's x-axis\n", + "\n", + "c = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "names=['Background radiation', '22Na and 60Co', 'Background subtracted']\n", + "hists = []\n", + "\n", + "lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "for idx, (acq, name) in enumerate(zip([acq_background, acq_sources], names)):\n", + " # print(f\"Current file: {acq.filepath.name}\")\n", + " ch = 'Summed' #acq.active_chs[0]\n", + " hist = ROOT.TH1F(f\"{ch} {name}\", \"\", int(BITS9*SUMMED_XR/BITS12), 0, SUMMED_XR)\n", + " hist.Fill(acq[ch]/len(acq.active_chs))\n", + " hist.Scale(1/acq.exposure)\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Counts/s\")\n", + " hist.SetLineColor(colors[idx])\n", + " hist.SetTitle(\"Background source subtraction\")\n", + " # hist.GetYaxis().SetRangeUser(0, 1e4)\n", + " lg.SetHeader(\"SIPHRA Channel\")\n", + " lg.AddEntry(hist, f\"{ch} {name}\", 'l')\n", + " hists.append(hist)\n", + " hist.Draw('sames hist')\n", + " \n", + "idx+=1\n", + "h_diff = hists[1].Clone(\"Background subtracted\")\n", + "h_diff.Add(hists[0], -1)\n", + "h_diff.SetLineColor(colors[idx])\n", + "lg.AddEntry(h_diff, f\"{ch} {name}\", 'l')\n", + "hists.append(h_diff)\n", + "h_diff.Draw('sames hist')\n", + "c.SetLogy()\n", + "ROOT.gPad.Update()\n", + "for i, sp in enumerate(hists):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + "lg.Draw()\n", + "c.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4fe40546-908d-46cd-868c-f4c3e9bd0902", + "metadata": {}, + "source": [ + "# Bad resolution\n", + "\n", + "It seems like the energy peaks from $^{60}$Co, at energies 1.173 MeV and 1.332 MeV are too close to be distinguishable, making them bad candidates for calibration. The resolution is pretty bad, which is probably not good. \n", + "\n", + "$^{22}$Na has a second peak in between these peaks, at 1.275 MeV. Is this peak showing up stronger than expected, causing issues?\n", + "How can we get better resolution?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40216972-edac-4bb7-b6d1-90992749a62b", + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/260204_CC_BG_comparison.ipynb b/notebooks/260204_CC_BG_comparison.ipynb new file mode 100644 index 0000000..4e04e54 --- /dev/null +++ b/notebooks/260204_CC_BG_comparison.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T10:44:04.423897Z", + "start_time": "2026-02-06T10:44:04.420846Z" + } + }, + "source": [ + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ], + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Current Comparator background spectra\n", + "\n", + "We acquired spectra from channels 2, 6, 10 and 14 on the SIPHRA by connecting the two available probes, which output the sum of the signals at the SiPM channels 1 - 4 and 5 - 8, respectively. We present a comparison of the data thus acquired. In this part we used the charge comparator on the SIPHRA to trigger readout. Since the charge comparator threshold is global, the only parameter in which the configuration of the active channel and the inactive ones differed was the value of the parameter `enable_triggering`. All other parameters are reported in the following table:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Parameter
Value
`cmis_detector_voffset`
127
`cmis_detector_ioffset`
7
`cc_threshold`
1
" + ], + "id": "f22322a36b757cda" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T10:44:08.728130Z", + "start_time": "2026-02-06T10:44:08.023861Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "dfs = []\n", + "dfs.append(pd.read_csv('../data/260204/1_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260204/2_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260204/3_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260204/4_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_Background.csv'))\n", + "\n", + "# dfs[0]" + ], + "id": "75bebada911337a", + "outputs": [], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "cb4c1aad-40db-456b-a316-8ce713563841", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T10:50:04.960837Z", + "start_time": "2026-02-06T10:50:04.332499Z" + } + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N_BINS = 512\n", + "BITS_12 = 2**12\n", + "\n", + "summed_spectra = [df[ch].tolist() for df, ch in zip(dfs, ['Ch14', 'Ch10', 'Ch6', 'Ch2'])]" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "code", + "id": "436876e4-6c49-4cc6-9552-6551a7059e27", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T10:50:07.969066Z", + "start_time": "2026-02-06T10:50:06.517005Z" + } + }, + "source": [ + "import numpy as np\n", + "times = [164.365, 148.329, 161.756, 119.645]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.14', 'Ch.10', 'Ch.6', 'Ch.2']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Current comparator background spectra, SiPM-Channels 5-8')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 5 + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e68ab9ee-bdeb-4c34-b3ef-e032a4b3a6ec", + "metadata": {}, + "outputs": [], + "source": [ + "dfs_ch1_4 = []\n", + "dfs_ch1_4.append(pd.read_csv('../data/260204/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_Background.csv'))\n", + "dfs_ch1_4.append(pd.read_csv('../data/260204/6_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_Background.csv'))\n", + "dfs_ch1_4.append(pd.read_csv('../data/260204/7_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_Background.csv'))\n", + "dfs_ch1_4.append(pd.read_csv('../data/260204/8_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_Background.csv'))\n", + "\n", + "dfs_ch1_4 = dfs_ch1_4[::-1]\n", + "single_ch_spectra = [df[ch].tolist() for df, ch in zip(dfs_ch1_4, ['Ch14', 'Ch10', 'Ch6', 'Ch2'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "97eb2049-7aab-4804-a96b-555d5d4c84f9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Jc7ks6Lv4vBZZsPoQr7+q410bsX8RiInMlzgXRFa+uroW98VxFMdTZEdFICHat1783il7TaL+q+uGrSxwqu29U5dj2hBi/yKLWb7HgYuJwEi8HtG7QU3tMK+++mr5S4gIsqr6Eh4aGmqrG9FVlDB58mT5HhDtWi8mylTXtryibkSQKbr8EueZyFQ2NvE+F+fVxce5/K8OFxN1K9qwimBddCkm2lE3hMgyl9+nOP9Ee36RSRftecVU1s2ZeC+K84RcDwNZchri5yvxgSk+lMWFIuInfvHNW1zoIP7ICuKiD5Et+/XXX+UfAvGzXUO76BE/E4qg5L777pMfjGLf4iIMEcDUlfjDJJ4nyiL+iFSVqRUBusjyiT/E4kImsU8ROJQFHeIDv+zCGXExhciyiO2KrMalEH+IRXZEvLZ//vlHZkbKfhoVwYgIfMRPrCILK15zXQIf8TxxAdPGjRtlWUVAe3EWWFzwJi7cEz9NimyO+GMksjniQiHxPBFEi9coLgwSP+/Wl/jJWvwxE92z/fe//5VBobh4rqx8IggV+xCTKJ+ohzLip1+RmRMX6ok/fGIb4uKxiwMAcbxENklkZMsu5hEBrshaivNFXHwn6rTsorSasqHi4rI2bdrI1yzKLoIXcSzKXzglzmtxYZxYLjJO9TkHBVGPYhtl57IIIGq6YE4QzSfEBVgiyBaBs3jPlX0hKCMuZhPvQTGJ81TUn8jAiiYW4qIecX6JzLX4EiSOg2gCIM4LQVxsJjKFYj9iHyIQFj8LC+ILkAiExT5FEFjTF4vajmltRKZUHANxcZL44irKIsok3m/iAqdLJYIr8R4S74u6EtlJ8dklAi9xnERAJn4lEZ8LoqlHWca7NqJuxPkpXpOoo/IXczYW8R4QF4yKbsFE/YnjKz43RPazKuLCTnH+ic9v8X4X75WLz6vyxDbF6xevWRwrcb+mwRVEdlx8oSmbxHur7P0pmvWQC7J3I12i+hBdt4iulcRFE+LiCtEt1MyZM20XZogLL5588kl5oUxoaKjlvffeq/Jir/IXzlR3kZggLo4SXcWIi0fExSD9+/e3XbhU1bYuvrBBXHQhLroSF5HU1v2W6BpHdPck1h02bJh8rVV1vyUuoqiq+63aLowoX9aybnfEbUREhLwgRVzYU/6CMdFVkNineN2iayzRJVT5C9eq2kdRUZF8HeI1tGzZUta/KFtZ90mC6PZJXECiVCpt3W+JehL7F93/VNf9VlXH52KiTI8++qjsqku8JtENmOjiqUxaWpq8kEZciCS6GxLLyuqhjDh248ePl88Xx+Oqq66ydSt18Wv++++/5XqLFy+Wj0+dOiUvYhLnZs+ePS1ff/21rHdxsV35er+YOJ6iW7Xqut9KSEiQF9aVdWm2ZMmSKrvfKu/i86JsG+LCQnHBjXjtNZ2T4kK1Hj16yLoKCgqyTJo0yfY6yi6gWbp0qbz4UbxecXFSWTdlgrgwT1z0I+pZo9HIY3HHHXfYtiF8//33sjunsuXinCsjLpwS73NxMVL57rcuvkiqLsdUPK/8OXjxOSveX6JLsrIuoXr37i1ff32636qp7i9W28VeZZ9ln3/+uezWquwYiHPrl19+sXVBWNvnz44dO+R5KI5Pv3795HOr6g6uprJXVdaL9ysurBOfWeL8FV3oiXKWXbx5cZnEBX5t2rSRZRL7FnVfdvFZdXV1cXdstV1AWx4v9nJ9CvGfvYNpImpeImMiMiiOOiKaqxBthkVWUjQ5aOwuhuxJNAkQvySItpbVtYV0FKIbLpGJExlWUWYici2O/QlERORERF+mov2iaI8ofhIVP6WLn9tdKYh1NuLna9Fum0EskWtiIEtE1IjZP9FmMCEhQV6gJXpsePPNN1m/diTaWouJiFwTmxYQERERkVPi711ERERE5JQYyBIRERGRU3LbNrKi02zRhq25GAyGCkOIEuuE5wnfO/ws4ecr/+Y0H/4dds46EX1Ji3JWx20DWRHENmfXQytWrLjkDuxdDeuEdcLzhO8bfpbw85V/c+xnhRPEJmJ45ZqwaQEREREROSUGskRERETklNy2aQERERFRczObzXCkQVVNJpNd969QKC5p0BgGskRERETNEMCKwVLEwCmOdL3Q8ePH7V0MeHp6onXr1g0KaBnIEhERETWxtLQ0Gah16NBBZiEdQV5eHvz8/OxaBpGdTk5OlvUTHh5e7+e7XSC7aNEiORUXF9u7KEREROQGRLCWk5ODmJgYqNWOE3oplUqoVCp7FwNhYWGIj4+Xt/UN8t3uYq9JkyZh4cKF0Ol09i4KERERuUkgKyaNRmPvojgkUS9ldVRfjvO1gIiIiMgFVRWg6UtNKDWZm2R/GpUSnhr7Z1rri4EsERERkYMTQezlb61Fen71I1Zdiha+Wmx8cqRTBrP1xYwsERERUTMSmVgRxG59ZhR8tI0bihUYjBj83zVyH7UFskajEa+88goWLFgg28qKrriGDx+O8ePH47XXXsOuXbtq3d/y5cvx4osv4uDBg5g5cybefvvtKoeZ7d69Oy6//HL88ssvaEwMZImIiIjsQASxvp72azc7Y8YMFBQUYOvWrQgMDJRdhC1evBhZWVl13obohWHevHnyQvrquhZ78MEHZXCcn5+PxuZ2F3sRERERubu4uDgsWbIE8+fPl0FsWS8G4qL4tm3bymytCEB79eqFbt26VZud7dixo1ynut4YfvjhB9kbwRVXXNEkr4OBLBEREZGb2bNnjwxYQ0JCqlweGxuLKVOmYP/+/bLJwLPPPlvvfZw9exbvvvsu3nzzTTQVBrJEREREVEGnTp3Qr18/eX/w4ME4efIk6uu+++7DW2+9BR8fHzQVtpElIiIicjN9+/bFqVOnkJmZieDg4CqHjS0jLgQTTQ3qS7S9nTp1qrwv2uKKwajGjh2LFStWoLEwkCUiIiKyA9HDgL222b59e1x77bUy0Pz6668REBAg+3H97rvvUFpa2ihlKX/RmNjHsmXL2GsBERERkTMTAxaIvl5FN1lNoYWvVu6jNnPmzMFHH32EgQMHyou1RCArut8aN25cjc/r3bs3/vzzT0RERGDdunW44447kJeXJ5//008/Ye7cubjuuuvQHJiRJSIiImpGon9XMWCBvUf20mg0ePnll+V0sYkTJ9ruiz5g4+PjbY/37dtnuz9ixAgkJSXVuq+7775bTo2NgSwRERFRMxOBpjuMvNXU3C6QFR32ikk0OCYiIiIi5+V23W+Jjn4XLlwInU5n76IQERER0SVwu0CWiIiIiFwDA1kiIiIickpu10aWiIiIyO5K9YCppGm2rfIANBcGNHBlDGSJiIiImjuI/aAnUJDaNNv3CQMePlBrMCtG63rllVewYMECOXqXyWSS/ciOHz8er732Gnbt2lXrrpYvX44XX3wRBw8exMyZM/H2229XWC62M3/+fHn/tttuw6uvvorGxEDWRWX98APylv6B0KefglefPvYuDhEREZURmVgRxP7fYUDr27j1YsgH3utq3UctgeyMGTPk0LFiKNnAwECYzWYsXry4wohctenQoQPmzZsne4TS6/UVlm3YsAE//vgjDhw4IAdcGDp0KIYNGyaHqW0sbCProvJXr0bx/v0o2r7D3kUhIiKiqogg1tOvcSdt3QLjuLg4LFmyRGZLRRArKJVK2btT27ZtZbb2wQcfRK9evdCtW7dqs7MdO3aU64hA9WI///yzHATB29sbWq0WU6ZMkYFtY2IgS0RERORm9uzZIwPWkJCQKpfHxsbKwHP//v2yycCzzz5b730kJiaidevWtscxMTFyXmNiIOsGmVn94cP2LgYRERE5kU6dOqFfv37y/uDBg3Hy5MkGbUehUNjuWywWNDYGsi4s6O67oVCrkfPLYnsXhYiIiBxI3759cerUKWRmZla53NPzQvtacSGYaGpQX9HR0YiPj7c9TkhIkPMaEwNZF6bt2BFe/a3fpoiIiMjBiAuz9HmNOxny67Tr9u3b49prr8XUqVORk5Njy5h+++23Dc6+Xky0t/3mm29QWFgIg8GAr776CrfeeisaE3stICIiImpOop9X0UWW6F2gKfiEWfdRizlz5uCjjz7CwIED5cVaIpAV3W+NGzeuxuf17t0bf/75JyIiIrBu3TrccccdyMvLk8//6aefMHfuXFx33XUYMWIEJk+ejB49esjniSC2tm3XFwNZIiIiouYkusUS/bzaeUAEjUaDl19+WU4Xmzhxou1+9+7dKzQR2Ldvn+2+CFaTkpKq3ccLL7wgp6bCQJaIiIiouYlA001G32pKbCNLRERERE6JgSwREREROSUGskRERETklNyujawYC1hMxcXF9i4KEREREV0CtwtkRZ9mYoqMjLR3UYiIiMhNGUwGlJpKm2TbGpUGWpUW7sDtAlkiIiIiewex4xaPQ0ZxRpNsP0QXgr9v+tstglkGskRERETNSGRiRRC76uZV8NH4NOq2C0oLMOaXMXIftQWyYtjZV155BQsWLJDD0JpMJjkgwvjx4/Haa69h165dddrn4sWL8dJLL8FsNstBEcRgCTExMWgODGSJiIiI7EAEsT4ejRvI1seMGTNQUFCArVu3IjAwUAaiIijNysqq8zb27t2L5557Dv/8848c6UuM8CVGCWsu7LWAiIiIyM3ExcVhyZIlmD9/vgxiBaVSKa8jatu2rczWPvjgg+jVqxe6detWbXb2nXfewWOPPSaDWMHPzw9eXl7N9joYyBIRERG5mT179siANSQkpMrlsbGxmDJlCvbv34+ZM2fi2WefrXK9w4cPIzExEVdccQX69OmD559/XjZRaC4MZN2AMTMTpoJCexeDiIiInESnTp3Qr18/eX/w4ME4efJkleuVlpZi9+7d+Pvvv7F582bZTOGzzz5rtnIykHVxnt26o3DjRqS+/rq9i0JEREQOom/fvjh16hQyMzOrXO7p6Wm7Ly4EE00NqtK6dWvcdNNN0Ol0sknBjTfeiB07dqC58GIvF+c3biyMaWko2mk9qXKLS+GhUkLnobJ30YiIiNya6GHAXtts3749rr32WkydOhVff/01AgICZI8D3333ncyy1tVtt92GpUuX4u6775bPX7Vqlez5oLkwkHUjp9ILMO79jQj102LTU6PsXRwiIiK3JAYsEH29im6ymkKILkTuozZz5szBRx99hIEDB8qeBkQgKoLQcePG1fi83r17yy62xAVet956q7wQTFwQJjK34vmiN4TmwkDWjWQVlqDEZEZKrt7eRSEiInJbon9XMWCBvUf20mg0ePnll+V0sYkTJ9rud+/eHfHx8bbH+/bts90XPR28++67crIHBrJEREREzUwEmu4w8lZT48VeLij7p59gOHYcUCjsXRQiIiKiJsNA1gVlffe9vMjL7+qa27gQEREROTMGsi7KZ+QoKMt1nUFERETkahjIEhEREZFT4sVebqK4xIQ3/jxi72IQERERALPBAEs9+mutD4VGA6XWPS4kYyDrJsRACBkFJfjk9r6Y+eNeexeHiIjIrYPYuNGjYUrPaJLtq1qEoP3q1bUGs2K0rldeeQULFiyQfcCaTCbZD+z48ePx2muvyf5ha/Phhx/i888/l91wiemZZ57BLbfc0oivpmYMZN1IkLcHurfyh0UEtkWl8PeqvbNkIiIialwiEyuC2Pbr1kLp49Oo2zYXFCBuxEhrtreWQFYMXFBQUICtW7ciMDAQZrMZixcvRlZWVp33JwZC2Lx5M/z9/XHmzBk59O2gQYPk0LXNgYGsG7HAjPVnl8Ev6ByGvaXCnNv6YnjHFvYuFhERkVsSQayqkQPZuoqLi8OSJUuQmJgog1hBZFQnTZqEdevWyWztgw8+KINUcf+bb75Bv379Km3nyiuvtN2PiopCWFiYDGibK5DlxV5uxKA8jdm7XwXCvpYB7D1f70RcWr69i0VERETNbM+ePWjbti1CQkKqXB4bG4spU6Zg//79mDlzJp599tlat7l69WpkZ2fjsssuQ3NhIOvKzGbAkC/HTtaXmmGRjQpkalZmY3091XK+vtRk75ISERGRA+nUqZMtAzt48GCcPHmyxvUPHjyIe+65Bz///DN0Ol0zlZKBrGtb91/gv5EwrngOhalxaBPiXWmV6d/tRpcX/samE03T4JyIiIgcT9++fXHq1ClkZmZWudyzXF/04kIw0bygOocPH8Y111yDr776CsOGDUNzYhtZV5aTiO0tb0fpqWNor8nD9MtaYvPGiqsk5xSfvy2yTxmJiIjclLgwy17bbN++Pa699lpMnToVX3/9NQICAuQvuN999x1K69Et2JEjR2QvB6LngjFjxqC5uV0gu2jRIjkVF1sDOFenV/nB2LoDSvduhOblj4BR9i4RERGRexP9vIouskTvAk1B1SJE7qM2c+bMwUcffYSBAwdCrVbLQFZ0vzVuXM1D3Pfu3Rt//vknIiIiMGvWLOTm5uKpp56SkzB79myMHTsWzcHtAllxNZ6YIiMj4S700WGImNgaZ2ILK8zvFOaLw+fy4KN1u9OAiIjIbkT/rqKfV3sPiKDRaPDyyy/L6WITJ0603e/evTvi4+Ntj/ft22e7v2rVKtgTIxh3Yjbj8oNm5PtaL+76efpgeXvnvO12LhgREZF7kYGmm4y+1ZQYyLoJ8XOB8uhpzDwKlKrMwIv2LhERERHRpWH3W27iULQCW7qpsPvycCjO98JFRERE5MwYyLqJAm8F1kzpieG33CwfG0wGexeJiIjILSgUCtuvo1RZWb2U1VN9sGmBC8opKsGC1cfwaJeL+nwrKYTX+heRixa46per8NeNf8FL4yUX7TuTi+t6maDzUNmn0ERERC5KDP0qLqwSfbYGBwc3KGBrCmazGSaTye5BrKgXUT+inuqLgawLyi0uxd7EXKS00qNAXBHpUdaY3AI/sxnpFguy9FnQm/QykB3bLRzvrTqO6CAvPDCinZ1LT0RE5Hqio6ORmJiIrKwsOIri4uJmHYWrOiKIFfXTEAxkXdih5FyctvhgRI8WSC2puEyjUGPWX3djzvjvcMeg1tibmAOjyWyvohIREbk0Dw8POQiByII6ShOD1atXY/To0XYtg8hONyQTW4aBrAszmi3waFeC+/UL4WksRStVC9uyn5LP4s7wEqQkbYN/u+bptJiIiMjdXUrQ1hRUKuduUshA1hUpSqFt8RfeiUlHCPIQ6BkMz6JMjDx3wrZKR4MeGvGN0NQ0nTETERERNTXH+lpAjcKkzIVCWQoPswKnFTkYFjUCv/V7Hvfm5gFaX5HHB6LFYAiO0diciIiIqCEYyLqoPkGj0QXelRdovAGlGpjyN0Tz7v/b+iJun98HnsXb7FFMIiIiogZjIOuiooK8MNg3ED4KDToHdQZa9gIi+wMdxtjW+SqrCE+nJEFfWgRTKYepJSIiIufCQNaF3eURga0xt+GmjjcBQW2Be1cDVzxhWx6l9sHwYj2CLdZGBkv3n8X2U5l2LTMRERFRXTGQdWdTVgD3bwI8/dG1pR9C/bT448BZZBYYYDDat4NkIiIiotowkHVnfhFAeA9511urRq/IAKyMTcVlr63GrB/32rt0RERERDViIEsVpOUb5G1Krp41Q0RERA6NgayLEUPNwlLLgTWbUbRnT/MVioiIiKgJMJB1MV9tPg2lUoHoYK8ql6uDg+EzfDgSbr8Dppwc2/xfsw8i15jcjCUlIiIiujQMZF2QUiGmqgc7UOp0iPzoQyh9fXHq+okwpqfjYQSjxGxEgXoPVEoF2raoov9ZIiIiIgfDQNYNKTQatPtzOcyFhTBmZqKbyhsd8jPQ1bsAJ98YjyfHdrJ3EYmIiIhqxUDWTalDQqBQqawPxv8PUGmBrFP2LhYRERFRnTGQJSCoDaD1ZU0QERGRU2EgS0REREROiYEs2VjEZBH/ExERETk+BrIktVB4YE7OPnx/5HvWCBERETkFtb0LQI5hhrY1irTeSMhLQAsfe5eGiIiIqHbMyJKkVijgbSwFzCb5OKOgBA/9sAfvrDzGGiIiIiKHxIysq0qNBQo3AS061rhawcaNyF+5CkHdOgGxf8OiVMAzLBfJublIydPDbLHgsavYrywRERE5HmZkXdXZPUD0IKDflGpXUYeGIv2dd5Exdy7ysyOBVpdhYeFJzNw0CRNGr8b6J0Y0a5GJiIiI6oMZWVfWbhSgC6x2cczPP8Gs1+Ps40/Ix9d7xaAgPxkq3wjsz01oxoISERER1R8zsm5M6eUFdVCQ7XFM29F4NiMT/ZIPAbnJdi0bERERUW0YyNIFncYBjx8H+t7NWiEiIiKHx0CWiIiIiJwSA1kiIiIickoMZKlGYsTahTvPcOhaIiIicjgMZKlaYX6emD68LZ5cfEAOkEBERETkSBjIUrU0KiUeH8vBEIiIiMgxMZClOvl1TxLMZgtri4iIiByGSwSy2dnZmDp1KqKiouxdFKdVsGkzjNnZlearlQo8MroD3ll5HIfP5dmlbEREREROEcjOmjULMTExUCgUOHToUIVlJ06cwJAhQ9CxY0cMGDAAhw8flvMDAwMxb948dOrEn8EbIuCWW6A/eBA5P/8sH2uVapxQlOLVpbfJ4/DI6I7w8eQgcERERORYHC6Qvfnmm7Fp0ya0bt260rLp06dj2rRpOH78OJ588kmZhaWLhH8Co7kUXvU4tH5jr4JX//7WLgoADOv3EB736Yp1GQdYvUREROSwFBbL+ejFwYis7LJly9C9e3f5OC0tTWZiMzIyoFarZXdQLVu2xLZt2+S6wujRo7F69eoqt/fuu+/KqUxOTg4WL17cTK8G0Ov18PT0bPL9PJb8HL6cr0F0fyMy+1yJs6Ej6vS8oB9+gDGkBfLGXiUfZ+RsxFeFv+HJVtY6e2yTEX1bKDCpvRIeKoVT1YkzYZ2wTniO8H3DzxJ+vvJvzgUiaZmUlITqOM3vxWfOnEFERIQMYgXxk3d0dDQSExNlIHv//ffj6NGj8vaJJ55Au3btKjz/0UcflVOZyMhIjB07ttnKv2LFimbZ3+NfPQcvTy/4+VoQ0b07evSp2z7Prl0Hj9bRCDlfxn2HMoEdv2P0mNFQKVXICz6DN/48grvb9sKozmFOVSfOhHXCOuE5wvcNP0v4+cq/OXXnNIFsWfBaXvlk8qeffmqHErmuQI0vspXA9Uuux7IblmFSvyjM23Ta3sUiIiIictw2stURPRKI1LLRaLQFsSJLK7Ky1DgMp0/DVFAo77f2CsNP2UYk5iWyeomIiMghOU0gGxoaij59+uD777+Xj0X7VtGkoKx9LF0a78GDULhxEzI+/vj8jBD46PNsF4ARERERORqHC2Qfeugh2X5VZF/FxVvt27e3Lfvss8/kJC76evPNN2WXW9Q4/K+/HgGTJsFs0FtnBLUFbv9F5L5ZxUREROSQHK6N7Jw5c+RUFdFP7NatWy9p+4sWLZJTcXHxJW3HLag9Ks3acDwDQ9qFwFOjskuRiIiIiBw2I9vUJk2ahIULF0Kn09m7KA6pJD4epSkpVS67tX8Ulu4/iyX7kpu9XERERERw90CWquc9bChKk5KR+t83bfMsCgU+3PMhXt76Mm4ZGI7urfyxcFcS4tIKWJVERERkVwxkycZ7wAAET7kHRTt2IOs760V1whcHv8Avx39BfG48nriqE/SlJiw7cJY1R0RERHbFQJYq8L/xRviNH4/cpUsR4OGHgcV6DCkxw8tsBvJT0CPSH11b+rHWiIiIyO4YyFLFE0KrhdfAAfK+l9oTX6ak4bPkJGhFN1yGPNYWEREROQwGslQ9nzCgzx1A33+LcdXwW/J6GEwG+Hpq8MWGU1i48wxrj4iIiOyGgSxVT60Frp8DXPchZhaWYvm5Ldh+bjueGNsJV/doiX1JOaw9IiIishu3C2RFH7KTJ09mP7L1NKnYiHDPYHlf56FCuJ9nUxweIiIiojpzu0CW/cg2kEoL5CUBBemNe0CIiIiIGsjtAllqoDt/A0TPBbmJtllnsoqQlnd+SFsiIiKiZsZAluomtDOgvHC6DGkXjFPphXjpj1jWIBEREdkFA1lqkCHtQ3D/FW1xLlePVGZliYiIyA4YyFLlk0Kng+HYMZx7/oVKy/bEr0apPlfe7xkZgNRcPWYu2MtaJCIiombHQJYq8R42DGH/+Q8KNmyoMP/qlkOxsCgByze9Jh/3igrAi9d1Q3GpibVIREREzY6BLFWiUCig7dSx0vx7x81FT6U3jJbKgeuWuAwcSrZmaomIiIiagxpu2I+smIqLi+1dFJdx5FwebvtyOzxUShx//Wp7F4eIiIjchNtlZNmP7KUzWswVH5st8rbEVHE+ERERUVNyu4ws1Y3K3x/GzEycuu56tF26xDY/HGq8fu4fGGK/gcliwoCwoXjm6s7w9dTgP78dZPUSERFRs2EgS1XStm2LmB9/RPzkyRXmP6sIRZFfBN7e9bZ8PCJqLz4a9RHO5libaqTk6hHuz+FriYiIqOm5XdMCqjt1UGCleRq1B7Rpxy7MsLYqgJ9Og7YtvDHi7bXIKSphNRMREVGTYyBL9TP+bbTR+EMBoLVfa9tsH60aq/7vCuhLzTAY2VaWiIiImh4DWaqfgChMCeyJA5bWuMOvM2CqnH19d+Vx3PvNTqRxxC8iIiJqQgxkqf763GG9PfQrkBZbafHPu85g9ZE0HEhiv7JERETUdBjIUv21HgzcvQzoNAEwX2hGoFQA1/WKwLD2IWgVoGPNEhERUZNyu0BWDIYwefJkDohQVxYL8laurPOIYB/+qw++v3cg/HWaSzhKRERERLVzu0CWAyLUnbpFCwRMnozkWQ/LPmWrU2oqxSf7P8Gi44sqzJ+7Lg4TPtyI7aeqfy4RERFRQ7ldIOvqrjhYCmV2TqNsS+HhgfAXnofSxwfxt/4LxoyMSuvEWfSYd2ge5u6bi1e2vmKb3y3CD3sScxCXVoCNJzJgsZzvp4uIiIiokTCQdTF3rzGg6PJB8IpqnDaqCrUabZcvgyk3F2nvvgdzYaFt2ZW+7dBOocWcfXMqPe9/k3oh/s0JuP+Kdvh4bRzu+Xonur7wNx5ftF8uF4Ht8ngznv/9EIpKjI1SViIiInIvDGRdUMH4MVBqGu/QasLCEP7CCyhYuxaFO3fa5rfQeOMGZeVBE8qbdWUHTB3WBuuOpaOoxIQj5/JgMJrw6ML9WHrajO+3J+B4akGjlZWIiIjcBwNZqhP/ayZAHRpacaZPKHB2T43PUykV8PZQVZiXU1SK3/YmY2ZPJUJ8tDwCRERE1CAMZKnhul4P9L+3Xk8Ro36JQFY+PUiMD0ZERETUMAxk6dJofevULZcQFaRDap4ekz/bylonIiKiS6a+9E0QAYrzvRLcv/p+vD/ifXiqPW3VcveQGAR4aXB5hxbywq7rPt5cocrMFgtKTWZoVPxeRURERHXHyMGFHEhqnG63amLKykbhli2wlJTIx928IzG0qBj35ubh+3Op2Jy8GXkleRWeE+jtgXuGtkH7UJ8qtznrx73o+NxfWH88vcnLT0RERK6DGVkXoS814ca5W/ATgCBvjybbT8qLL8JSWorQJx5H8NSpCPfwx6ep1gDU1MBtJmUXy9vErKJGLCkRERG5OgayLsRotkC0RtVpKvYS0JhEECuYi/XWGV7B1lvPAEB/ISNcNgBCWfvYMhEBOnRt6YcQXy0UyKqwrNBglM+7+DlEREREVXG7pgWLFi3C5MmTUVxszQI6i2JjMTKKK4+sZXetBwOPHATu+dM2K8eQg1lrZ6HXt72wIWlDhdVFd1t/Pnw5vp0yoELAGhmow5t/HcWCHYnNWnwiIiJyXm4XyE6aNAkLFy6ETtc4I181l4f+eQgjF47EluQtdiuD95Ah8GjTBtpOnSouCIgGPP1tD+/+627sTduLy8Iuk+X+Pe73Wrf93ISumNwvEgu2J8qA9lhKflO8BCIiInIhbhfIOqv0Ims71Ay9/bKyYU89iXZ//QnPbt1qXC+/NB+fjfkMX1z1Ba5pew2SC5Jr3bZSAUwb3g69ogKw4Xg6/vvXkUYsOREREbkitpF1Atd/vAmntYVQNN01XI1CtMx954p3kK3PRpegLlAqlPBQ1b3QoleDN27oIbOyK2JTmrSsRERE5PwYyDqBk+mFMLeyBoqO7qqYq+q1/q39o3AwORc9IwMqzBcDJ/z7qx3o0tIPT1/duZFLSURERK6AgSw10pmks04fXQZMWw9oq+4z9mKPXXVRe9vzjqbky+lQci4DWSIiIqoS28g6m6zTwJ9PAKmxcCjewcCMnUBmHGCoeKGWyWzCwfSDKCgpqNOmfD2t36+8PVSyS7G0vPNdfRERERGVw4ysszm5Fji6VvTQCox/y27FsBgMKNiwAZ7du0MdFGSd6RdxYYWELUDiNqgUJnxx8As5dQvuhsLSQnQN7orZw2ej0FyI1MJUhHmHVdj2NT1bok2INzzUStwzfydGv7see1+4CipxRRgRERHReQxkqUEyv/4amV98Ad+rxyHyvfcuLFBqgJ/+BZzdKx8+GN4dvfvfj2S1EnP3zZXz8kvy5cAHb6W/BcNiA367/jfE+MfYNiH6l+3eytqd128PDcGA1/85P8ACA1kiIiK6gIEsNcz5Eb4sesOFeUoVcO8qIOsUoFACIZ0QsnQmrsvLxeq2A+QqAdoLF3UVWgptgS0RERFRfTGQpXrzu3ocTFlZUGg0sBiNFRdG9LFOZUK7yJvRrUfj75v+xtmCs3hi/ROsdSIiIrpkvNiL6s3n8ssR9ekn8Bo8qF7Pa+XTCl5qL5SYSnAs+5htfom5RPY9a7aYkVmcKS8OIyIiIqoNA1lqMNGWVR8bi5zfax+Ctnww2y6gHW5bfptt3uPrH8fwn4fLeSMWjsCnBz7lUSEiIqJaMZClBvO79lp4Dx2K7O++r33ltKPA19cgYPe3+Hrc15jaYypGe49GkGcQMoqtw+7GZlq7FBM9GRARERHVxu0C2UWLFmHy5MkoLi62d1GcnsrHB75Xjqp9xZxEIG4VEL8ROLgIKqUKD/V+CNdpB0IpeyMAWuhayNsQXUjFfSisPRXMWLAX+lI2OSAiIiI3DmQnTZqEhQsXQqfT2bso7iF6MHDsT2DNaxXnWywYtu9RoNCajX1m4DNYf8t63Nzx5gqrBfto8dO0Qfg7NgWZhSXNWXIiIiJycG4XyFLjs5hMsJRUE2T2uR0YMB0wVh6dS2MqRPvSEnipPBHtGy2bGSjO9xUr+o3dmbITiXmJGNQ2GOcTs0REREQ2DGTpkqjDwmA4ehRHe/ZCwfr19X7+Fynp2D7iU3QK6lRh/vaU7ZiyYgruXXkvjxARERFViYEsXRJdjx7osGkjfMeMxpnp91e9kk+o9dYruOaNnVoHv4O/YfnJP2RPBp4qTxhMFwZcmL/pNAxGtpMlIiIiKwaydMnUISEI+89/ql+h/73AwweACe/UvKHY3/CvkzvwvXdPfDzqY7wz4sL6r03sjh+2JyL2bB6PGBEREUkMZKnpiQauga0BlRYoKQQyTwLfXg+LaA8rhrItRwWgi9oHvUN7V+jB4PaBreGn40B0REREdAEDWWpU+WvXVr8wpCNgKgU+HQacXo+tPf4LeFu73aoLrVqFp345gITMwsYpLBERETk1BrLUKFTBwfC/4QYkPfAgjOnpVa8U0h64fSFQWgSodcj3jql5o4Z8FOqz8dPPE2Exm/HNlAEoNZlx5BybFxAREREDWdeUdxYoSGvWXSo9PNDytVflfYvZOshBlUK7APeuAaZvqNSs4GIdzCrclpOL1/UnYTKVoE2IN/x0msYuOhERETkpZmRd0dY5gFIFRA2EQ4q8DGjRsdbVNEo17sqrmH311Kjw0tLD2HcmpwkLSERERM6AgawrMpuAMS9bf8p3RhlxwHc3AtvmVlr08b/6ICLAEweSGMgSERG5O14GTvalVAPLHwVu+PTCvDPbLtxXVfyuFerniSBvLYpKTHL0LwWH/CIiInJbzMhSo8v+6UckPfJ/ODXxBpyefAsKd+yofuVbfwAKUoG5g4DdX1e72lexX8MkMs0AooO88OZfR7FgRyKPHhERkRtjIEuNeDYpEfLgA3Ko2uID++F/3XVQenmhaNt2GLOykPrW/yoPYxvRB/i/WGDyt0DPW4Aek6zzPf3lTbDSE09kZuOj/XOQVmS9gO25CV1wS78onM0p5tEjIiJyY2xaQI1G/MzfYtYsOZUxnDopb/NXrkTWV1+hcNtW+FxxRcUnigvTul5vnUqLrbet+gEFKVBYzLjri1F4OzjowupKBdQqBY8cERGRm2MgS45FowO6XGu979cSyE+1d4mIiIjIQbld04JFixZh8uTJKC7mz9LNpWDTJugPH6kwT1yoFfrBh4gbOxalqc3b5y0RERG5BrcLZCdNmoSFCxdCp9PZuyhuIejOO2WTg5yFC+Vj49lzyF60SN73PHUKpQmJMKam1NyrgRg4wWIBvr0eyK9hXSIiInIrbhfIUvPy7NQJ3kOHyPtegwbBb/zVSP/gw7pvwDsYuPcf0QAXKMwAcs40XWGJiIjIqTCQpeY72bRa+N9wY/2f2KovfDx8MCXYC2mG7KYoGhERETkhBrJkdxmffY74W26F/tixatf59bpfUagAzumzmrVsRERE5LjYawE1uYDJk+XFXb6jRlW5vOCff+Rt8Z49silCVcK9w6GxNGkxiYiIyMkwkKUmpwkPR+jDD8v7xQcPWWeWljZsY+aSRiwZEREROTM2LaBmZy4owIkRI2HW6aDw8LDNz12+HCcnXIPcpUurfqIYOOHPJ4Ck3bZZItN7KDkXWYUMcImIiNwNA1lqdha9HqasLCS/9CKU/n7WE9HfH8W7dsOYkYHC7durfqJXMBDYBkjYJLvjMpot+GT9SVzz0SZM+3ZX874IIiIisjs2LSC7sZTLxoa/8Dw8u3RB3rJlKE2tbjQvBf4MbIHOa15Hr1aleHFvdzm3b3QA8vXGZio1EREROQpmZKlZadu1lRd/hTz4gLVv2LIT0dMT2rZtrYMfVGNaz2lYbs7Gxg5DMP7sRxho2oUJPVti5qgOzVR6IiIiciTMyFKzUnp5oeUrL1sfrFiB0Mceg+HYcXgNHFjrc2/pfAt+i/sNJ8IHoGeeBTfkJ8G7W3jTF5qIiIgcEgNZsquAiRPrtf6QiCGYf+R7hAe1wQ3tIoCuYVh7NK3JykdERESOi00LyCGZCwthMVZu9zqr7ywMbDkQepjsUi4iIiJyHAxkyeEUbtqMY5f1Q9LDj9i7KEREROTAGMiSY1EpYTzfa0FpcnK1qxVaTLAcWQqcWt+MhSMiIiJHwjay5FCC7roLmrBw2bQg47PP5MAJqqBAtPnlFyiU1u9dUb5R+ODMOvj6tcAtB34GOr9k72ITERGRHTCQJYei8vFBwE03In/tWpgyM+U8Y0oKYDYD5wPZx/s9jqLSIqSmHbNzaYmIiMie2LSAnI5SoYRKobJ3MYiIiMjOGMiSc7BYqp5fkAq1Ibu5S0NEREQOgIEsOSQxypdHu3bwGTkSSl9fnBg5Cqa8vIor+bYEzu5Fp53P2auYREREZEdsI0sOyaN1a7RbvkzeFwHs8QEDYS4ogMrP78JKgTHAlS/Cc/OXCDQzK0tERORumJElhyeDV4Wi6oXhPaAtSsELRW80d7GIiIjIzhjIknNr1RdHB7wOnaXY3iUhIiKiZsZAloiIiIicktu1kV20aJGciouZwXN2+9L3ITEv0d7FICIiIjtxu4zspEmTsHDhQuh0OnsXhepBFRSE0zfdjNKzZ+Xja9tdixxDDr44+AWUCgVKTWa8s5IDJBAREbkTtwtkXZXRbIRXm/cgelv1qqbLVWfWdtkfcmQvY1qafNw7tDeuan0VjmQegdkvB8E+Hvh+W4K9i0lERETNiIGsCwWyKs9UeKm90MJSzRX+TkwdGAiFquJoXuNixsFb4425Z/6Gt4fbtZIhIiJyewxkXYxC4dqHtDQlBQUbNsBiNCLGPwbXtbtOzhfNC/L0Rtzx5XaYzS6YkiYiIqJKXDvqIZeT/PgTODNtOnJ//73CfA+1Ej/cOxCb4jJgZCBLRETkFhjIknMxGuWN2WCotKh9qI8dCkRERET2wkCWiIiIiJwSA1lyevH6dKxXWTO1RERE5D4YyJJTGxk9El29I/G+R4m9i0JERETNjIEsObUgzyDcEDrQ3sUgIiIiO2AgS85PqQSMBvj8/TD8UGjv0hAREVEzYSBLTsOze3eoAgOhiYysuCC8F+AbDs9DP2KN9jHAzPayRERE7oCBLDmNqLlz0HHrFmg7dqy4QAxkpgtE5gOxCFHkYf2xFPy8M5EDIxAREbk4jutJLueZHzYhA/5oH+qLy1oH2rs4RERE1ESYkSWnVLRtO4xZWRXmWTReSLEEYp32/xCIPDHHbuUjIiKipsdAlpxOwI03QH/kCLK+/bbiAg9vDDJ8DB0M8FZUHvmLiIiIXAsDWXI6vqNHw3vY0GoSrgpYoEBvRVzzF4yIiIiaFQNZchlatRI+WjX+sgzGe5q50GUcsneRiIiIqAnxYi9yGb6eGqx/YgTMlhHIfKcrlCY2LyAiInJlDGTJpQT7aOVtir0LQkRERE2OTQuIiIiIyCkxkCUiIiIip8RAloiIiIicEgNZIiIiInJKDGSJiIiIyCkxkHUyFguHXSUiIiISGMg6CV9Pa09p2UWl9i6KwyjavQuGU6egVqiRXJCM93e/X2F52KHPgcJMu5WPiIiImhYDWScR4KWRt8zHnq+PG29ESXwCTo2fgA5L9uGBXg9g8YnFtvqarbkf3hkHgPiNdjpiRERE1NQYyJJT0vXsiQ7r1iL4vntRvHsPBkcMhslsQkZxhly+XdkXpV5h9i4mERERNSEGsuS0FGo1NNHR8n6ILgTeHt647vfrYDQb5Txmr4mIiFwbA1lyCSKQ/WnCT8gvyZcXxIX7eyIurQCnMwvtXTQiIiJqIgxkySUtuG8QdBoVsgtL7F0UIiIiaiIMZMkleWpUUCjsXQoiIiJqSgxkiYiIiMgpMZAllyYu+Coxmu1dDCIiImoCDGTJ5UxfPR3X/349spQW/LY3GR2f+ws/70y0d7GIiIiokVmHiyJyAWqlGiqFCrtSdkGpUCJNrUJmjvVir/jMInsXj4iIiBoZM7LkMvy1/vhhwg9YMGEBWni1sHdxiIiIqIkxI0supVtwN9v9MD8tbusYDe/SSLuWiYiIiJqGSwSyBoMB06dPh5+fH0wmE+bMmWPvIpED0KpVGNghBCtPqOxdFCIiInKHpgWzZs1CTEwMFAoFDh06VGHZiRMnMGTIEHTs2BEDBgzA4cOH5fzFixfjiiuuwIcffojAwEBs3brVTqUnezAXFMCUk8PKJyIicjMOF8jefPPN2LRpE1q3bl1pmci6Tps2DcePH8eTTz6JqVOnyvkJCQky+BXatm0rH5N70LZvD8Pp0zgzY0bVKxxeAp0xr7mLRURERO7YtGD48OFVzk9LS8OePXuwcuVK+fimm27CjBkzEB8fL4PesuBVPL766qsrPf/dd9+VU5mcnBysWLECzUWv1zd4f0ajEcXFxfJ+UVGhvE1ITMDRctsrNJbY1i0q1uPQ7t3IPGmCI7uUOinP89ZbEfDHHxW2pS/W47hnb3Q68Sei1b7Y5DMOK1Y4/hecxqoTV8I6YX3wHOH7hp8l/Hx1mkC2OmfOnEFERATUamuRRdOD6OhoJCYmyqD2gQcewIEDB1BSUoLBgwdXev6jjz4qpzKRkZEYO3Zss5VfBCcN3Z96ywrodDpkFwJeXt5AJtA6ujVal9teTnEhXlwIWT9eOi/0u+wyoP1oOLJLqZPyCjw9kbZ2LXqV29bbv7yNjsOnQrcmC4H5gWgT1RZjx3aGo2usOnElrBPWB88Rvm/4WcLPV6cPZMuC1/IsFjFuE6DVavHVV1/ZqVREREREZA8O10a2OlFRUUhKSpI/nZcFsSJLK7KyRMaMDJy4YgROXT8RFpNjN6kgIiIiNwtkQ0ND0adPH3z//fe2ngrEBV5lF3mRezNlZ8OYmgrDsWPA+UD2UMYhW9Y+JVePohLrlyAiIiJyDQ4XyD700EOy/arIvo4ePRrt27e3Lfvss8/kJLrfevPNNzFv3jy7lpUc15XRV+KDPR9ggzkfkYE6rD6cijf/OmrvYhEREZErt5EVgxlUN6BBp06d2Ecs1cnTA57GgfQDMOTkoldkAKYHtcWpDGuPD0REROQaHC4j29QWLVqEyZMn27qzIjdgMiA0/zA0Zr29S0JERESunJFtapMmTZKTaL5AbmLdbEwuLUTLoBtxOqMHgn084OepsXepiIiI6BK5XUaW3FCptUlB26yNeOvd2bj3m132LhERERE1Agay5PS8Bg9G6BNPoOVrr1a7Tmz4DdhraofbVP8gu9A6ChoRERE5Nway5PSUHh4InjoFPiNHVl7o3ULepPl2wXpzz+YvHBERETUZBrLkMhRi+GKVCon33gdzUZF1Zq9/Ac8ky4wsERERuRYGsuQyVP7+aPPLIhTt3AljVrZ1pkKBDLMehwtXQa9mrwVERERu3WtBYWEhNm/eLAcs0Ol06NWrF7p27Qpn6n5LTOx+yzV5dukCKJXImj8fqh5mOe+LA19gY/YCtApuBaTbu4RERETU7BnZhIQE3HPPPWjbti1mz56Nf/75B7/88gtuvvlmdOvWDfPnz4czEF1vLVy4UAbh5JpavvIKcn/7DS2TipBenI4cQ46cb4F1uFoiIiJys4zsnXfeiUcffRRffPEF1KItYjnx8fFy6NiPP/4YM2bMaIpyEtVZwE03Iv3jjxHlF433d78v50X5xCDApETfvKO4rmQ5gCtYo0RERO4SyG7YsKHaZTExMfjvf//bWGUiahRTu0/FzL4fy/ufH/gcCTmnkK2NwjVxf+LNv6bAX6fB/Ve0hUKhYI0TERG5y8Vep0+fbvySEDU1pQpZESNQajLj0/UnMfvvozAYre1oiYiIyE0C2f/973+NXxIiIiIiosZuWtC6dWt06tRJ3rdYLDh27Bjmzp1bn/0QOaScolKE+6vsXQwiIiJqqkB2zJgx+PLLL22PH3jggYbsi8hheKiUaOGrxci312HjUyMR4qO1d5GIiIioKZoWvP322xUef/LJJ3BWog/ZyZMnsx9ZN+epUWLz06NgNJtRZDDZuzhERETUVIFsQEAAXAX7kXVvncJ9EeTtgVcndrd3UYiIiKg5L/YS/cgSOTOtWiWbEVzfu5W9i0JERETNGcju27fvUvdH5LC+2HAKA99YjT8PnrN3UYiIiKiput8ickW7E7KRmmfA/jPWIW2JiIjIsTGQJSIiIiLXHqK2rA9ZIme1L20fBsevxBXaUrxp78IQERFR82Zkf/rpp0vfI5GdJOYnosCkx1FlxWFpt57KqNeXtITMQizZlwx9KbvtIiIicppANiwsDN999x3y8/PlY6PRyCwtObXJ/aLw/O+x2ByXiQKDsU7PeWrxATz80z78tCOxyctHREREjdhG9vXXX4ePj48cUKB9+/YIDw/H5s2b67sZakQmswVfbT7NOr1ISXy87f717a7H1O5TcV/kmArrvH5DDznC14M/7MauhCy0beFdaz0aTdbsrdHMpjZEREROFch6e3tDoVDgzz//lEPXbtu2Dc888wychSuO7JWUXYSP18TJ+wp7F8ZBeA/oj3MvvoiCTdYvWWHeYXjkskfQ17dNlevn6Y14e1IvXN4+xDZPNDf450gqYs/mNlu5iYiIqAkDWa1Wi3Pnzsn2srfccgvatGmDwsJCOAtXHdlLrWQHFOVFzJ4Nzy5dYC4oaHCdbj+dhanf7MKd83Y0whEiIiIiu/ZaILz66qvo06ePbFIwcuRImbUquIRggagpWYxGGNPToQoJkb8k1EepyXpRWKnRervmaCrO5uhhYu8dREREzhnIXnnllUhJSbE9PnbsmAxoiRxR6ptvwpSRgbDnnkPQHbfXur5Wo8Ivu5PQKdxXtp0t74Hv98BwPqglIiIi+6vz79Hbt2+vcn6nTp3w6aefyjanhw8fbsyyEV0yEcQKxtTUOq0/c1R7XNGxBT5bfwp7EiqO8MVLu4iIiJw0kH3rrbcwevRozJ8/X2Zhc3NzZWZ23bp1ePLJJzFw4MAKmVoiZ+TrqcHDozsg0FuD91Yft3dxiIiIqDGaFixevBg7d+7EZ599JrvgSkpKkj0Y9OzZEzfddBO2bNkiu+UicnhmI7Zt+R+8241Bj5Du6B0dIJsMtA2xnr+tg71x82VR2HYqy94lJSIiosZqI9u/f385ETmt0M44rVHhvhPfQn38G2xPLcScmbsBr77VPqXEZMaUr3fCaDJDrVRU6D92Z3wWnv/9ECb2aYX7r2jXTC+CiIiIBPbZRO5Fo0PZ5VpGhQIWfRagt/YTuzFpI57c8CRiM2MrPEVka9ccTcOP9w1CywDPCst2xWfjaEo+1h1La7aXQERERA3stYDIVf164lesTlyNcK9wRCsmV1reI9LfLuUiIiKiqjEjSy5L6eFhvaO2fl/THz8O//tfxb/WmS6spNBcuF96frQ3o75Zy0lEREQN43YZWTFErZhcaYhaqlrEW7Nl8FqwZq18rD94COoTieiXDfw4otyK2fFATgKQer5JQfJuIOYueVf0Jds2xFv2ZuCh4vc+IiIipw9k09LSZBdcl19+OYxGI8xmMzzKsl9OMEStmCIjI+1dFGpimogIORVu2FDzigsmA6YSILQF4K0DLBcGPfDzVOPn6YOrfNrKw6k4nVEoLwAjIiKi5lfvFNOvv/6KAQMG4M4775SPY2NjMXHixKYoG1GTUSqU0Kl1uDmiBfIspQ0a8mDH6Sz0iQrAM+O7NEkZiYiIqJED2TfeeAO7d+9GYGCgfNyrVy8kJCTUdzNEduWt8cbSiUtxRq1CrrJub4MP9nyAQv9vAGURLu8Qgm4RfvjXgGgEepVrZ0tERESO27RAqVQiODi4wjxnaVZAVF64dzjq0yjg29hvUepZApW2Hyb1G4rrekXI+b/uSWLFEhEROUNG1tfXF6mpqVAorCHA2rVrbdlZIkdlKSmB/tjRKpcVKJVIOt+zQZl0fSK8YuagwGtZM5WQiIiImjwjO3v2bIwfPx6nT5/GiBEjcOLECfzxxx/13jFRc8pasABKnQ4l/boC8YcvLFCqcV90W+SaiuFrunCRV3LxMah0Z1BszsI/if/gyugrbct8dRrZkwERERE5WSAbExODNWvWYMuWLbBYLBgyZIjsuYDIoZWWwnf8eOQMigHeKRfIKhQyiBXyy3Wv1SbEG7ozMWjlE43nNj2HK2+7EMh+eVc/dA/n4AhERERO17Tgqquugr+/P66++mqZmQ0ICJDziJxBsC4YKoUK3YK71bheiI8WPSPC8NboR5qtbERERNREGVmRdS0pKZF9xorBBEQ2VsjNzUVRUVE9d0vUfHxGjoQh7iR8Rl8JP09PKPxj8MVVX1S7vjizcw25PERERESukpF9/fXX4ePjg4MHD8Lb21veF1OXLl1w++23N20piS6Bz+WXo/W338BvzJg6rf91YRz+t+t/iPGPYb0TERG5QiD74osvymzstGnT5G3ZlJOTg+eff75pS0nUxK436zCisAjPZ2Thcm0Y5lw5B88Neo71TkRE5EoXe33yySdNUxIiO7rRrEPftAx5f3LQYCByOI8HERGRqwWyGRkZePnll7F//37o9Xrb/B07djR22YicRlJ2MTadyMCwDiH2LgoREZHbqHevBVOmTEFkZCRSUlJkk4LQ0FCMHTsWzmLRokWYPHmyvGCNqC6MZiMeWfsISs2lUCkqv2VE8Noh1AcvLDnECiUiInLkQDYxMRFPPfUUPD09ce211+LXX3+Vfco6i0mTJmHhwoXQ6XT2Lgo5gAjvCHgoPRBkqf6toDfp5aAIn2cXI7ykBMg8eWFhfgpCd7yFRztlNk+BiYiIqOGBrIeHh7zVarXIysqCWq1GUhLHmifn9Ov1v2LjrRsRU4dWNj0LznfJVXo+m39kGfBOJ2DjO4je/x7O5erxzspjTVxiIiIianAb2U6dOskA9o477sCgQYPk4Ah9+vSp72aIHIJWpQVUVSwwGoADPyMmtAvu63EffD18oUt8quI68RtxuOMovKspxlUFRZh5ZXvMWROHx67q1FzFJyIicmv1CmRNJhMiIiIQFBSEhx9+GP369UN2drYc5YvIpcT+BiydCU2Lzpj10HbrvN/KBbLH/gKO/42tUZ2xPT8OZoUW/+kaJgNZIiIicsBAVqVSVeidYOjQoU1RJiL7s5gr3paXfw44vhYI7Qa0HggcOtzsxSMiIqIGtJEVF3jNnj0baWlpcmjasonIpR38BTAbAbUW2PIhELcaaD0E8PCxd8mIiIjcVr3byD7++OPy9plnnoFCoYDFYpG3otkBkaNTqNUoTUpG/B13wCMqGi1ffw0KpRIYMA0IiAYM+cCRpUBO4oUnFWcDi6cC/e+FUn8Yc6DCf8+cRngVXXERERFR86n3X+Lyw9OK4LXslsgZeA0ahIi3ZsP/mmuQ+9tvsJSWWhf0nATcPA/mMbNRWBAF074lgIevtaut726wrjPmFbw5fDZSvAKwd/BUoO9dFbZdWJoPtPoQI7+/FY/8vAv6Ur4viIiImhJTSuRWxK8HflddBd9qBvHI+HYxEr87ibT9fkCrPsC/fgLO7rUt79miJ1r4tgLajQK0FZsVZBkyofQ8gwxTLH7fn4jkHA66QURE5FBNC4hcmbnYOuyyIUeDxJ+S4ZW5A1UNOptnyMPWs1thMBls8+LzTjRjSYmIiIiBLLk9Y3Y2TNnZ0LZta6uL4kwPILMYpX+vQ8iQylX01s63UGIusfZDC2A/DNh74FUYCzpC7XPc7euUiIioObBpAbm9xKlTcWr8BBTWY6hlEcQOazUMvVv0xki/jihRAKFeEShO/pdc7o98t69XIiIihw5kc3NzcejQocYrDVEzy/zyS5iysuV9U+75IWjraFDLQfhy7JcYE9BZPlaYS+EJa1OD1R5PQlWY2gQlJiIiogYHsuPGjUNOTg4KCgrQq1cvXHPNNXjhhRfquxkiu1IFBCDsP/9BxsdzYExJubSN+UdZt1mYAg2sPRV4KQzwPfar6OajMYpLREREjRHIpqamIiAgAH/++Seuv/56nDhxAr///nt9N0Nk994Lgu66E1DX0kxcpQF63w4MfABQ66zPhULeKsv6kfVvZZ1vscDHUyXvf2+8EoE73wYStzbp6yAiInJn9Q5kS8/3u7lhwwaMGTMGGo1GBgVELkkEqxPnAle/CYiBEwD8Z+B/8PSAp3FD+xsqrgpg2YzL5f3PFDfD5BVe9RC3REREZJ9eC7p37y6bFxw9ehRvvfWWHJ6WgSy5k05BneRUFY3K+qVOvCeyikrgbTCCg9gSERE5SCArgtc9e/bI9rFeXl5ITk7GY489BmexaNEiORUXs7N6ajrvTO6JkkVmZGQWoRsrmoiIyDGaFkycOFFObdq0kY9btWqF9957D85i0qRJWLhwIXQ6a3tHopoY09OR/dNP9a6k/q2DwBY3REREDpKRNRqNKCkpgdlsltlMi8Vi64JLNC8gcjW6Pn2g69kTKa++BsOpUwh7+mkozreTrVZJQXMVj4iIyO3VOSP7+uuvw8fHBwcPHoS3t7e8L6YuXbrg9ttvd/uKJNej0GoR+vRTCH/pRWR/+x0seuvwtdUym4AvrrTeV3nIm2+2xKP7iyuw9lhaM5SYiIjIvdQ5kH3xxRdlNnbatGnytmwSfco+//zzTVtKoiai9PSUtwoPa+B5MXHRlt+YMRXmpc+Zg5PjJ6Boz56LVgYQah0cAWrr0LWJ2UUoMBhxIpUjfREREdn9Yq9PPvlEBrApKSmyuUGZ6Ojoxi4bUZOLWfgzzLm5SH1zNjLnfQVDXBwUXl6w1NBcpnDzFpScOoXi/Qfg1bfvhQVB7YAJPwA/DuWRIyIicsRA9ptvvsHMmTOhVquhUqlsWau0NP50Ss5He/6iRUF/6BCC/n0XlH5+yPjoY7uWi4iIiJogkH3llVewY8cOdO58/idUIheiiYiAWW+wdzGIiIioKbrfatGiBYNYcjmq4GDrbZD1tj56teiFAeEDML7teNu8hLyERi0fERERNUJG9sYbb8THH3+M2267DZ7nL5QRxOAIRM6q1dv/gzEzE5pWrZD5+Rf1em60XzTmjZ0n75eaSmVge/vy2/GVteUNEREROUpG9umnn8asWbMQEhICX19f2QWXuCVyZkqdDh6RkbK9tyogQM4ru60PjUqD78d/D7VSDbNaCZ1GheggfskjIiJyiEC2fNdbJpPJdkvkKgImT0Lb5csQ8cbrNa6Xu3Qpjg8dhqxvvqlyeZC3B+bf3R89I/2bqKRERETurd6BrCB6KNi4cWOFEb+IXIXIymrbtYOyluYyhiNHYMrMRPGBg7Vu82yOHiVGcyOWkoiIiOodyP76668YMGAA7rzzTvk4NjYWEydOZE2Syzv3/AtIuOceGbzWxd8aC0wWM3pHBeCnnYmYuy6uyctIRETkTuodyL7xxhvYvXs3AgMD5eNevXohIYFXaJMLU4ghu4C85ctRtHUbSupwvk/rOQ3zPS3YkxuHey9vi9sHtka+/sIAIkRERGSHQFapVCL4fFdFZTyqGd6TyBWo/P0R+fFHiHjnbdmrQV3c1/M+hLAlARERkWMFsqKHgtTUVNmOUFi7dq0tO0vkqnxHj4b/hAm27CwRERE5YT+ys2fPxvjx43H69GmMGDECJ06cwB9//NE0pSNyFRYzvI3ZgMkIqOr9tiMiIqIq1Psvatu2bbFmzRps2bIFFosFQ4YMQUAD+tskcieDUn/CmKSPAK8ZwNiau/UiIiKiJmpa0KFDB9x7770yiL366qsZxJJb0kRHW2+joqpfSaEENr4LpB6Gb2m6nLVm5z68v/p4cxWTiIjIpdU7kE1MTMSECRNkE4OoqCg888wzOH6cf5jJPSjPj2IX/tyz6Hw4FsFTpyB/1Sqkvfd+5ZV9wwAPbyA11jar0GDElri6dd9FREREjdy0wNvbG3fffbecTp48iTfffBNdunTh6F7kFqK/+BzGtDR4du0qHwdMngxjRiaKduyovLJSbQ1kxShfXtaePXw9Nc1bYCIiIhfWoKtOxGheS5cuxfz587Fjxw488MADjV8yIgekDgmRUxmFUgmPqMiqA9lyOob5AqeB9qE+AEd0JiIisk8gO2vWLCxcuBB9+vSRWdnFixezH1kiIiIicvxANiwsTI7s1aqOHcMTEREREdn1Yq+jR4/K22effVYGs+Vt3rwZzmLRokWYPHkyiouL7V0UchdntgNGvb1LQURE5L6B7G233Wa7P2DAgArLZs6cCWcxadIk2TRCp9PZuyjkDmIuB2J/BXbPt83KKDDgti+24ZlfD9q1aERERG4TyIp+Y6u6X9VjIjqv67VAmyvkyF5lTmUUYsvJTPy+N5nVRERE1ByBrKLcGPPl71f1mIisSk2lyLCUsjqIiIjsebGXXq/HkSNHZPa1/P2yZeQoLICF/Ts5imc3P4uM4gzM9vbC+MIiexeHiIjIPQPZoqIijB8/3va4/H1mZB1DpzMWWPSFQH4O4MdeJRyBCGKFNJXK3kUhIiJy30A2Pj6+aUtCl0RjKsXLP5igbWWC5sFfgdAurFEHpYMej6kX4ZyiJYBx9i4OERGRe43sRY5HtFJWWoCAYQaofP3sXRyqQW/lSdyr/gtFFi2A91hXRERETX2xFxFdIv8oViEREVEjYkaWqDlc+QKQXwQc2cb6JiIiaiTMyBI1B5WHuCqSdU1ERNSIGMgSNQJTdhZKEhJqXknrh5bn/sFczQcosHiy3omIiC4RA1miS+TZo4fsUzlp5qyaV+z7b2wc9i3uKXkSU0qeYL0TERFdIgayRJdI27Ytwp99FpaSklrebUrkBPbEPkt7ZMCf9U5ERHSJGMgSERERkVNiIEtkRyVGM+ufiIiogRjIEjWigvXrUbh9R63r6TTWIWuHzV4DfamJx4CIiKgB2I8sUSMRvRacmX6/7Gar0+5dNa4b5O0BXakSafkGlJjM8Dwf2BIREVHdMSNL1FgsFtutxXz+/nl/nv4ToxaOwpb03y7MZL+yREREl4QZWaJmcCTriLw943EUQBvWORERUSNgRpaoESi01gEOFJ6egFqNM/dPh1bPC7mIiIiaEjOyRI3Aq28ftP7uW6iCQ2ApMSB+0mT4XR4KcAAvIiKiJsNAlqiRePXvf+GBihdvERERNTU2LSCyF7MRPRSnWP9EREQNxECWyA4yFEEwRQ3Cbx4vACUFPAZEREQNwECWyA6KFTrob/wWaoUZMPOiMCIiooZgIEvUjMq6jlWyD1kiIqJLxou9iJpRS39PvHVTT3Ru6StGTmDdExERXQIGskTNSGRiJ/ePQqm5FEX5uax7IiKiS8BAlqiJBHsGA0iVt5n6TNv807mnMemPSYjyjkS5AWuJiIionthGlqiJvDTkZWy4ZQP6h5frXxZAelE6DCYDkgqTWfdERESXgBlZoiaiVWmh9Qxk/RIRETURZmSJmlDGZ59j4ivr0TGp6gu7TBYFdH9MBwzsS5aIiKi+GMgSNaGCjRsQlJyPTslVB7KTS16AOn49kMdmBkRERPXFQJbIjnZbOgFKDY8BERFRA7CNLJEDWHbgHNZkFmOQJ/uWJSKihvt5ZyK2n87Ck2M7I9zf0+WrkhlZoiZiOBlnux+abYHGWH2Q+svuRPy25wyOZDOQJSKihvti42n8uicZG0+ku0U1MpAlagLew4Yi+f8ehf5QLFLbBeLyWAtu2GKudv3Xi1/HCe1diNIf4/EgIiKqIwayRE0g6uOPoYmIgEWvx5ERMVjTSwHPkurXb2VJgVphRoAxjceDiIgu2eFzeTAYTS5fk04fyGZnZ2Pq1KmIioqyd1GIqqRgvRARUTMa3rEFFu9OwndbE1y+3u0SyM6aNQsxMTFQKBQ4dOhQhWUnTpzAkCFD0LFjRwwYMACHDx+ucVuBgYGYN28eOnXq1MSlJmqYq2LGYkDLAega3JVVSERETe7eYW1wZZcwGIzVN2lzFXYJZG+++WZs2rQJrVu3rrRs+vTpmDZtGo4fP44nn3xSZluFlJQUjBgxosL0yCOP2KH0RPUT7BmMzoGdoVPrWHVERETO3v3W8OHDq5yflpaGPXv2YOXKlfLxTTfdhBkzZiA+Pl5mcNetW9fMJSVqON9Ro1C0Ywc8u3WF/qJfHsorMHvAF4UwWNifLBERkdP2I3vmzBlERERArbYWSzQ9iI6ORmJiogxkq3P//ffj6NGj8vaJJ55Au3btKq3z7rvvyqlMTk4OVqxYgeai1+sbvD+j0Yji4mJ5v6ioUN4mJCbg6PntpRdbYDZbfz4oNRuxefMWFHgnwdFdSp04hT695XT8xAkEJMQjqzQbKSnAztydcrHJZG2Ef03R87g1uhBD0hbAZDS5dp00gMufJ/XE+mCd8Dzhe6cmhQVG7N69C2dTLDDnKLCi5KRLf544VCBbFryWZ7HU3q/mp59+Wus6jz76qJzKREZGYuzYsWgu4kRp6P7UW1ZAp9MhuxDw8vIGMoHW0a3R+vz2EjILodz+t7yvUaoxdOgQIKwbHN2l1ImzSd2zF4WZhQgPD0f/jv3x6cpPoVKp5LIESzjG3DQchi8XQqVWuU2d1JU7nSd1wfpgnfA84XunJv+LXY/LLuuKxL3JaB/qg7Ej27v054lDBbKi54GkpCSZgRRZWRHEiiytyMoSOTulQoXNZzfjSNYRqBUO9dYjIiInZjJbk34qpfv1k+NQf01DQ0PRp08ffP/997j77ruxePFi2aSgpmYFRM6iY2BHvDb0LlhgkYHs4+sft3eRiIjIyWUVlmDUO+vgqVZh3RMj4G7sEsg+9NBDWLJkieyJYPTo0fDx8UFcnHU4z88++0wGsW+88Qb8/PzwzTff2KOIRI1OpVBiVPQoef907mnWMBERXbK84lLkFJWKq2SgL3X9ARAcIpCdM2eOnKoi+oPdunVrs5eJiIiIiJyLQzUtaA6LFi2SU1kvAERERETknJx+iNr6mjRpEhYuXCh7ASAiIiIi5+V2gSwRERERuQYGskRERETklBjIEhEREZFTYiBLRERERE6JgSyRHYgBEQwmAzxbLhRjsvAYEBERNYDbBbKi663Jkyez+y1qdkW7duHk1eORPncuovyi8N9h70ATsAdQio6siYiIqL7cLpBl91tkL/rYWJScPo3CLVvk435hA3gwiIiILoHbBbJEREREriijoAQGo3s1V3O7kb2IiIiIXNENczdDq1YhKsgL7oIZWSIiIiIXkK834od7B6JNiDfcBQNZIiIiInJKDGSJiIiIyCkxkCUiIiIip8RAloiIiIicktv1WiAGRBBTcXGxvYtCRERERJfA7TKyHBCBHIUpPh5h2RZ7F4OIiMhpuV0gS+QISs+dQ+4tt+OdL03QGkvsXRwiIpeVXpSOB1Y/gGX5y+xdFGoCbte0gMgRmIv1gMkEDwAqi9nexSEicllHs45iU/Im6BQ6exeFmgAzskREREROzkNtDek8Ne4V2jEjS0REROTkQezymcOgLzWjdbD7jOolMJAlsjOFqgCpRWcRYO+CEBGR0+oQ5gt35F75ZyIH5N36CzywfhJOa0z2LgoREZFTYSBLZG9KPSywoEDJrriIiIjqw+2aFnBABCIiIiLX4HYZWQ6IQPagCgqSt0p//2rXUSsU2HzOgjVHU5uxZERE5KzOZBXhxk+2IMhLdOZYUaCXB95bdRwLtifClbldRpbIHoLvnQrfq8ageM8e5Pz2W5XrRATo0FIPHE8twKjOYc1eRiIiV2OxWJCY77qB3NmcYmhUCvz58OWVlj11dScUGow4lpIHV+Z2GVkie1CoVNC2aQOFR+VvzWWUCgU8+I4kImo0qxJW4Z1d76Bni54uW6salRJB3pX/tmjVKgR4a+Dq+GeTiIiIXFJhaSH6hfXDC4NesHdRqIkwkCUiIiIip8RAloiIiIicEgNZIiIiIidjMJqw8nDtvdzEns3DyfQCuCoGskQOZHTpGvgVue4VtkREzeHv+L8x8feJ+GjvR9CqtZWWv77tdbn8hiU34ED6gfptPPMk8PuDwNHlsKeVsalYvCcJU4e1qXad8d1bosBgxNsrjsFVMZAlchSX/RvelkK0TV9j75IQETm1XSm70CGwA14d+ipeHPxipeXrk9bjhg43wEfjU/9A9vASYN8PwLZPYE9iLMh2LXxwz9DqA9leUQGY3C8KFhceOJKBLFEzM6amoXjv3soLWnTEGWUUjwcRUSOI9I3E0FZDEaILqXK56JIrWBfMunZybjcgAoeoJXvy6t8fmogInHv2WR4IIiKiS+R2GVkOUUv2pAkPR8gDD/AgEBERNQK3C2SJiIiI6qUwA0jYAphNrDgHw0CWyMEUlpgQl1YAfSk/MImIGttdf92FtKI0eaFXnf16HzD/amDvd3Y/ICazRf6NMPBvhMRAlsiB6DTA3sRsXP3BBry2/LC9i0NE5HIOph/EvLHzZK8GdVZSWPHWjj7fcAqj312PV5c1zt8Ii8kEU4H9X1dDMZAlciARXgo8flUnPDWuM7ILS+1dHCIilxTmFQZnlV1UIm/z9MZG2V7KSy/jeL9+yF26FM6IgSwRERGRmypNTq5w62wYyBIRERGRU2IgS0RERHQpRI8GJ9eyDu3A7QZEcEUWiwVK70P2LgYREZHDMlqM2JC0ofE3rM8F5o+3Dhr78H4gMKbx90HVYkbWBaQWn4M67Bd539uVB1QmIiJqgCjfKHT37I6H/nlIBrSNymK2BrGCqZG3bSclZ87AlJtbYZ6poAAl8fFwNAxkXYBFvIks1kOpsndhiIiIHIyXxgvX+11v72I4hcLtO3ByzFVIuPueCvOTHpqBk+OuRv7q1XAkbFpA5KCOnMvD0DfXoNRkRpeWfvhmygB7F4mIiFycKSfHepudXXH++cfGi+bbGzOyRA7qVEYhfD3VeOm6bth2KtPexSEiInI4bpeRXbRokZyKi4vtXRSiWvlo1TIbS0RERJW5XSA7adIkOUVGRtq7KESVmJQewNaP0bqtD9opzPhf5nMI2jACwFWsLSIiezu8FMiMA1QeQN5Z4PMRgI/zjhLmCtwukCVyZMdi7kRkwW9ombMb3RRBaGM8hdJ4cRUsA1kioqaQpc+C2WKGUlGH1pZntgFdrgNaDwX+fooHxAGwjSyRAzGqfYCA1vYuBhGRW2gf0B7zDs3Dd4e/q/uTAlsDnv5NWSyqB2ZkiYiIyC3N6DMDuYZcmZUl58SMLBERERE5JQayREREROSUGMgSEREROQiLxYK/Dp7DnkTHGnjAUTGQJXJA7YoO4OrAJHsXg4iImtmuhGw88MMe3DVvB+u+DhjIEjmay+6GztsPVxf+bu+SEBFRVTqOBQbPAEY+1+j1YzRZrLdmM+u+DhjIEtlZjwTrh5ZNSAeg8wR7FYeIiGrjFQSMfR3ocq1L11XB5s0o3LYNjoyBLJG9eHhgXQ8FHv3NjPAsazC7qXATxvwyBn/lx/G4EBHV0f2r7scNS27A0xuflp+hS08uhbfGu07PFev9fOxn+bxP9n3COj/PmJKCM1PvReLd98CY7bjtddmPLJGdKFRKzL1GhUFHjVCd/wUpoTQBKfoUHNCE4GoeGSKiOtl6bqscnSsuJw4z+8zEgPAB6BbSrU7PfaD3AxgRNQIbkjZgd9pu1nhVTCY4KmZkiYiIyGW08W+D3qG9oVFq6rS+VqWV60f7RTd52ajxMZAlIiIiIqfEQJaIiIiInBIDWSJnce4AcIb9ChIRObS8s8DxlYCp1N4lcQsMZImcgBYlwBejgHljgMyT9i4OERFV5/cHgAWTgN1fs46agdv1WrBo0SI5FRcX27soRHWmhBkwn/92byphzREROSqj4fyt/pI3VWAwwmS2QKtWotBghL9Og6ISI5qSOT8PzsTtAtlJkybJKTIy0t5FISIiIqpSidGM4W+tRXGJCS0DPHEqvRDdIvwQezYP0UFeSMwqavSay1uxEueeex6a6GiUJiY6xZFh0wIiB2Qwm/CntxfOqN3uuyYREYnMqAXIKixBcalJBrGCCGKfGNsJdw1u3SR1ZExLg8+VVyLizTed5hjwrySR3ShwbdtroVIuRaBnYIUli/OOYlFoCPoX6/FZ43/pJiJyOePbjEeWPgvdgus2EIKzUioUTbsDxfnJSTCQJbKjNy5/A0dVK6BT6yrMN8M6ZK3RiT5MiIjs6eG+DyPCJ4IHwc2waQEREREROSUGskRERETklBjIEhEREZFTYiBL5NDYSJaIyDkogD8eBgoz7F0Qt8JAlsiRBbW1dwmIiKgu/r0USD8KZJ5gfTUjBrJOpMdpM1r/k4nSQpW9i0LNRe3JuiYicgYRfQGtDxyZxWRCxiefIHPePFjMZrgCdr/lRG7YakHLhDzkGnUIsXdhiIiIyKmUnj2L9A8+lPf9b7yx2vV03bqhxcOzoAoMRMpLL8ORMSNLRERERDYKDw+EPPAAAmoIdh0FA1kichm5hlyUmErsXQwiImomDGQd1NaTmTiemm/vYpCDKyox4p8jqcgsMNRpfaPJjLVH03A2pxiuZnPyZgz7aRgeXvuwvYtCRA5IAw2CPYPhr/WHr4cvHJHZbMHB5JxG325avl5+7u9NzIarYSDrgMSJ9q8vtuHmT7bYuyjk4D5ddxJTv9mFV5cdrtP6C3cl4Z6vd+L/ft4HV5NalCpvUwpT7F0UInJAWqUWqyatwtrJa2Uw64hWHk7Be6tOYHSX0ErLLu8Qgi4t/dA/JhCXtQ6U90d1rrzexcS6afkGjHl3PR7+yfU++3mxlwMyGM0Vboka61wxGE08t4jIbWmUGjgy8VnevZUf/jO+C1YfSauwbPrwdhjW4cKl3n89fHmdttkrKgDPXN0FDy3YA8fMQ18aZmSJiIiIyCkxkCUiIiIip8SmBUTuIP0YcHgJfEoG27skRETOJW41kJts71JQNRjIErmDje8CB35C96hbAFxv79IQETmPZf8HtOwJjHkZiPsH6Ck+R88LagMMngFofQGNlz1L6bYYyDopi8mMgnXr4Nm5s72LQo2g3aliqLfuQ/tjOVCUmBHbWgGDh4J1S0TkCIbMBFoPAXrcXHG+WguMfb3Rd2feswvReSlI9AtHczGcOg2lTlflssLNm2EuKIAjYhtZJ5WzLRFJ9z+As089be+iUD15tGkDXZ8+8Bs7Vj4Wt4N25MFz3q8YviYJDy4346bN7LGCiMgdRZ47idKHH8DbG+c02z69Bg6E4cQJFB84AJ9h5XpDUKvhe9VVyJz/NdQtWsCjXTs4GmZknZT5fDdKFkPdOsInx6EJC0XMjwtsjyP++wam/XYAzw16Dl9tmIvuy3dBY7RrEYmIyE7UxlJ5qzE33x8C/+uuRcBNN1War1AqEfnhB7bHiffeB0fDjCwREREROSW3y8guWrRITsXFrjdEJxEREZE7cbuM7KRJk7Bw4ULoqmnQTERERETOwe0CWSJHdTTzKCywVLnMok3GMQ/HHlqRiKiuUgpTsCV5C0xm6/Ue9WWxWLAzZSc2JW/CiewTjVbxOfocnC0422jbo6bHQJbIAQyPHI45++bgoP5gpWXnlIA5+jPcGhGOQgW75CIi5/fE+icwffV0LDu1rEHP35GyA9NXTcfsHbNx67JbYbZcek8vXYO7orC0EI+sfeSSt0XNh4EskQN4sv+T6B7SHSZUzk5Yr18FjAoFjIxjicgFGEyGCrcNeX6Mfwzmj5uPEnNJo5SpY2BHPD/o+QaXieyDgSwREREROSUGskRERETklNyu+y1ns3DXGRjOD35QXnJ2McSlP+dy9dh2KtMuZSP7+HFnImI6h2LryQykF1T8CcxstuC7bQnILDDA38sDdw+JgUrZNO0R9p3JwZojqbh9UGuE+Xk2yT6IiNzBmawiLBJ/700c1bG+GMg6MIPRjCd/OYCpw9pge2nF5Ll3bhxKoEJqbiH81r8EZQRPflel1IsvKsG2x213vIzcbZ7oDmCFcRLaKVSYfm4OsPc2nIm+AS/9EYtb+0fhk/UnMbR9MDqH+zVJud5bdRzrj6dDqVTgkdEdm2QfRETNypAP5J0FfrwNCIwB1FqgIF0McVX/be39AUjYCvS9C+g0rsZVv9kSjy83nZb3J6kaWnj3xKYFTmDWqA4on1NL8umJFATJ+zoYcJVqN1RgIOuStL7I7znF9rBo/Fxs9xyKTebuCFNkY5RyH65QHkDvws3A7m/kOmqlAv+9sSd0Gn4aEhHVS2YcUFoERPUHts0BNr0LjPwPEDmgftsZ+RzQT3x2W4CDC3kQmhADWSdQ/M9qXLE+G2qTtY/REpUXzlha2LtY1BwUChRFDZd3rzhgRtGONCzRXI1vTWNxytKSx4CIqNE/d1VA339feNzlGkBZz3ApqA0wcBoQNRCOKmfRL6I9GpwdA1knkPPS85jwdyY6J9m7JGQv2hILHlpuRsmHnyE8O4UHgoiIGsSkUKLFI48g/aOPoDnn/H9PGMgSERERuQkLgJD7p0Pp5QVXwECWiIiIiJwSA1kiIiIickoMZImIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickoMZImIiIjIKantXQCqndFsBAcbdU/nCs/hj3MrK8wr9twGlbkj1vrmIjgnDAqTxm7lIyJqartSdmFn6k55/4rIK7A+aT3ic+NZ8SQxkHUCJSYDdPYuBDWbNv5toLYo8b/hD2HbuW3IzU3HtJxcAD5yebFuDbxarMVaAL3NaeiQ3YZHh4hc1v92/Q8+Gh+kFaVh7r65iPaNRp/QPpjSfYq9i0YOgIEskYMJ8gxCt8AuCG8zDuPajMOZuIMI3LIAZ84HskRE7ua2Lrdhc/JmxOfFo29YX7w69FU5P6M4w95FIztjG1kiIiIickoMZImIiIjIKTGQJSIiIiKnxDayTkoPD3kbqchAiaVcnwYmg/0KRQ7lfbyFtj+kATEDgSN/ABpvexeJiOjSHVwMlHoDI56u19OUphLg8xFA3lnAwwf49x+Af6tqVlYDak/AbATU9bjcWutX8ZaaHANZJ7Xf3BYDcRje0CPN4o9SGcyaGciSTT8cgUd+IXAwERj+JGAsBs6ksYaIyLnJz7Id9X6ayqwHzu4F7vkbWHgXkHum+kDW0w94cBtgNgF+Leu+kzsWA/nngPCe9S4fNQwDWQekyM/DMzu+Rap3EL7qdk2FZRaFBbpDieiptT425KnhEWsBwhX2KSw1CcOJE0icPh3e/ftDv20z0uMCL22Dga2BjBONVTwiIufVqi+gPv9HtCZBDeja0CfUOjVATO453HN4OSJQv19WzXo9Ul58CQpPT4Q//xwU6rqFdgqlEoGLFkGfnQ2/a6+Fs2IbWQekjD+J4WcP4LqTm2zzVKM1+Hu0CQk3F8Dkp8OglMM43bIlAtsXQhPHw+hqirZuQ+H6DUh7+x0YD8TCK0qP/05SoqgOn71EROR8Lks7hkB9PvZ0Gwb1C9buxerCmJqK3CVLkPPzzzAXFNT5eZEff4Si/v3Q4rFH4TdhPJwVIyAnoQhV4lB3C0r9zSgN9ZfzTAolfFqyTayrU+h08G1fhL3t+XYlInJlZ31CsKvnCCg6dWnyfXlddhkKhg5F4KRJUHpYr7txRvzLSEREREROiYGsgzEYTdgVn13jOoUGU7OVh5xHWoEBO05n1Wldi8WCn3cmYu2xNKw6nIpfdifJeZfKbLZg4a4zWHM0tcb1Sk1mfL8tAVtPZjZ4XweTcjF/82kUGoz1el5uUSnmbTqNYyn5Na6XkquX6yVmFsHd7E3MxtebT6O45MJnTYnRjO+2JWD7qQvHbMvJDHkcxfEk98ZzoXomswXfbInH7oQLn88bjqfjxx2JOJScKz9nDiaLYcipIXixl4NZEZsqT/ARNaxjPP9HI8BLI28VCkCp5MVe7u5MVhGe+OUANKraz4X9SbnYn3SwwrxuEX7o0vLSuowRH8ZP/nJA3o9/c0K1660/lo7nfj+EFr5a7Hx2dIP29dySQ9h/JgcqpQJ3DY6p8/O+356A/604hss7hOC7qQOrXe/T9Sfx9ZZ4+YfmvVt6w5088+tBHE3Jh5eHGpP7R8l5a46m4fnfD6Glvye2PnOlnDfrx73IKChBRIAnRnUOs3OpyZ54LlTvXK4eL+6JRdsQb6x53PrX/f7vd6Po/BfFnpH+iPDXYUKPlsg/kI5/DYgGLI0b2PaPCcS1vSIQVewJrIZLYSDrYERWLDKw5j7rgn2sbVnahFj7BVUqFPKPOdGlnXuXXn+Weq53Sfs8/+T6bsNSz+c1Rqba2ZS9ZEuFI1q53mzruV8V0UV4LlSv7H1U4d1U7sGUoW0wsU8rZM7bj+LcQET2jUTc7sYNZEP9PPHRv/og/sBx1PxblPNh0wIiIiIickoMZImIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickpO32vB2rVr8f3330Ov1yMwMBAff/yxvYtERERERK6akZ01axZiYmKgUChw6NChCstOnDiBIUOGoGPHjhgwYAAOHz5c47ZGjhyJefPm4YcffkBCQgLy812tYwkiIiIicphA9uabb8amTZvQunXrSsumT5+OadOm4fjx43jyyScxdepUOT8lJQUjRoyoMD3yyCO25y1btgxdu3aFr69vs74WIiIiInKjpgXDhw+vcn5aWhr27NmDlStXysc33XQTZsyYgfj4eJnBXbduXZXP+/LLL5GUlITZs2dXu893331XTmVycnKwYsUKNBfR9KEu+zuQaq4yq1xsMFy4X1wMTzHUZk4ugrysHbabzReGiNy8eQsKvJPg6OpaJ+4iK8s6fGF2djYiys03lDv2dR0mVtTrINHj9vlxMg4eOgTv4rPIziuu9nlbtm7BGZ+6D6yRkWEdlSYu7iRWmE7L+6fzLvTyXdOx3Z9hPV9LSgy1ngPVnSe5udahaY8cOYIV+cdwqMj6605BQUGN2zwRb913ZmZmjeslJFhf39lz57BiRRrc6X2TX2Ct20OHYhGQdUTe35turTe94cL+S0qs64nPbWOi/S654GeJ/eukvudCXl6evI09HIsV8TWXMy83D/v27sMZwxnbvIyMDOxesQL5pgt/L9dvWI8gVVC12zHr9fJ25apVuLy4GAe2b0fOkYoDD5zLPAGzxdxodReTfBwlOdZ9FBYW2rZrMl0Y/vnAgQPQpR2C7/Fj8EhJQeyKFchJSEXHi7a1a9dOFJ6qXLfq9HTb34w1a9bA7G0dLKkquUnp6FLu87m+50mLjAwkxsai0O/SRoF02TayZ86cQUREBNRqa7FE04Po6GgkJibKQLYqixcvxssvv4wJEybg/vvvx6uvvooWLVpUWu/RRx+VU5nIyEiMHTsWzUWcKHXZn35fMvKOp1Sar9NqxeC01vs668hf/gH+tnpSKi+c3EOHDgHCusHR1bVO3MVPf/+EU6mnZFvv8rTy2NedGK5Y1Gve1gtBaY/u3YEMLQyJ54CMqp83ZPAQdI2o+4fTguQdQFY62rdvh7GjrR+5+87kALs3y/s1HVvl4VTg4C54eGgxduzoBp0nc09sAvJz0aVLF4wdEoPCE4VYtGURfHx8atz3iTUngNPHERwcjLFjqx+idqshFkiOR0TLlhg7tg/c6X3z7uENQGE+unfvhrH9o+U8y6FzwKE98NR6YuxY6xC1/9mxCigtQd++fXFlF/sNUcvPEvvXSX3PhXl/zENyVjK6de2GsZ1qLudXy75C7569UZhciG3Ht8l5ISEh8vVlFGfg5YUvy3lXDL8CET7l0wAVrVm2SN5eNWYMcFSHgQMHAtGDKqyzZWcclAfXNF7dbTqCM0eygEzA29sbY8dah6hVbfobMJ8forZnT4wVI3slJaO4pAT9xo5F3O7YSiNw9evXH8M6hFTaRUlCAk6evz9q1CioAgKqLU7ZyF5lr6++50niol/g260bAh3ob7dDBbJlQVl9hocUWVsxuYtktQoW5MpvaoUmA/Z7eaIlR6d1a0aFCT/5+sCz2AyUAEqfA1ibqIPSU40stTeuKyiEqhH2syphFZRQ4srW1iCmIXINufjz9J8wF3do8Da2JG9Bpj4TFlT/YX2pzuSdwUn9CihUreRjk9mEpSeXItI3Ev3D+zfZfl3d4czDOJh+ENe1vw46dc1DcZN9bUzaiLySPIxvM77S3+XqKD2TsTntHIZ2uAMGk0G+1we3HIzWfq2x7NQyBHkGYWiroRWek1KYgh+P/oiRUSMR7h1eaZt7UvcgS2/9taq5rU1cixJzCcbGOE7QRg4eyEZFRckmAkajUWZlRRArsrQiK0tWcwMDMO18ILsl7yTWtffFg7Dgzs63Avia1eTEbuhwA4pzitHKJxIW7Krz8077n8NvIUHoUGyEwpAFj4gfMGvtD1CE+8GiUCDMaMKQSyyb+GPz6DrrLxrrJq9DsC64QdtZcGQB5u6fi67+IgsysUHbePCfB2GymBChfApAxex1Y3l/z/vYV7wSmqArAHTEztSdeGHLC/D18MWWf21pkn26g5e3viyDWfEL0qSOk+xdHKpGqalUvs+E9gHt0SmoU53qyrPlYiyKP4tuLQORUpSCT/d/ihFRI3B/r/vxn03/gQIK7L9rf4XA+IuDX8jbY1nH8NKQlypt84kNT6Ctf1v0atELm5Otv/aU56/1xy2dbpGfCSG6ytnKS/nSPWvtLHm/W3A3+SWWHJNDBbKhoaHo06eP7E7r7rvvls0GRJOC6poVuKtWEBe0WdtNRhiNCLao8GCPaTjGQNapXdfuOmjjtAj03I/65B/KfrOwyL8NF37BEEHshfmXpvwvI5Zy+6gvM6ztLC3nbxu0DUvZcxu+jdqUvUaFwlJhn7X9QkS11Ov5+mM9Oo/6vd/Pv1/Ev3LvGdtxr2Fb1S0Tz53ZZ2a1QapGqcFzg55DY6vwmcf3vUOzS+v8hx56SLZRFdnX0aNHo3379rZln332mZxE91tvvvmm7FqLiIiIiMghMrJz5syRU1U6deqErVu3NnuZiIiIiMi5OFTTguawaNEiOYkurIiIiIjIedmv4z87mTRpEhYuXGjrwsqRecCIFR5PWh8UpMFc1iloJQpAUe5QqkUvs+Qy6njFsA4GPKxeXKd1Q5CLXz1ewMeaD6pfKXk3MHcIsPa/aGwqox5LPZ7Ff0yfwikUnu+zrLQZvgCveBb4ZBiQZu2/tUkcXwnMGQT8/Qzw6eXA8serXzdutVw3Ou77pisPUZa1L+oaZZ2y3qo8gMRtwB8PA/PGAj/eJjrQrvo5KYeAT4bKc3jAoReszxV/M8XfySUzrO8B8V4Q74nUWGDH53X+zK0TjQ7hqRvxgGopj3ETcbtA1pkoFRa8Ybzd+mDksyhWVtHJcUBrwCcUUJ/va/SO34Dgds1bUGpaCjUmG56vdbVkSwiGKGPrtMloRSr6KuMwTrmz+pWSdgNpscAJ6wAljUllLERP5WkMt9Swf0eSe36AEbO1L+cmdfxvIPUgcG5/0+0jYTOQfgTYNhdIOQAc+6uGdbfIdYNT2VsDNaH8c3VY53wf610nApf/H7D7a+DMNuDYcsByYYCBCs7utSZ6rnoNx2LuBqatA9QewO3WPmXle0C8F8R74uw+a4DrZ+12r1H0m4LTMZNxhaoJ389uzu2aFjg6VU42hu2y/lERF3323xELjfjbGdoZuDCoyQVaL1hMOUgrG7UsonfzFpiaxSFLW9lXRU2KLRUHTpiww4xWmRYoujZdR8PdMk6hxzfLUBR0H7z69m2y/YgRedL+9zZUQYGA94URy+oqd/lyFG7YiJCZM+AOzIWFSH37bWhaRiBk2n1Nvj/tjs1Inr8NIdOmQVvu4l2iJiEC0RY9676+VzDQYTQyTpnkYEEZn32O0pRzCAvxrZzN8/QFVBpYTCb8a50JahNQUroEZ0+dQ+hjj0IdXM+uB1Ua5PmJDjMb/9ofY0YG0t59r8IACOdeehkKD43tccj990Pbtq3tc6Fk3mdwNQxkHYxX7F54Zp6F52vPw+f3J5FYFIbveulwX5toYLd1nWP9Q+DbvhsW6jZBhK3GtDRkf/8DIt6aDfVFo0KR8/IZMQJe/fsjO7ItsC673s+/abMZPnogP7Tp3uZjEnchInEHcn8PbdJA1nDyJLJ/+EHeVz6lhqmegWzWt99Cv/8APLt1AyIrjuTjivRHjyLnx58AjaZZAlnvv5cgb+8OeES3RosZDGTJsaV/9BFgNML/1mB4VbOOJb8AN2y1dsFVsmOOGGsGPsOGwm/8eDiKwu3bkfvrr/K+wsMDLf/7BkqTkm3L8/7+G/krV0F7/3Tb54Jp8wbMHngXXKmhEANZB5TnG4So3j3ge1CPhYYrEdZjC6aVa7NT7KvBuZFDkLRrM3qfH8NOoVLB/7rr7FdoanQKT08ET52CvIxCYF3j/7xPRESuw3/ChAqPDSfjKq2j8PbBtogecCVsI0tERERETsntMrLsfouIiIjINbhdRtaZut8iIiIiouq5XSBLRERERK6BgSwREREROSUGskRERETklBjIEhEREZFTYiBLRERERE6JgSwREREROSX2I0tERERETsntMrLsR5aIiIjINbhdIEtEREREroGBLBERERE5JQayREREROSUGMgSERERkVNiIEtERERETomBLBERERE5JQayREREROSUGMgSERERkVPiyF7NpMRkwZt/HUV0kBduGxhdYdmexGws2ZuMkZ1DseN0Fno3V6HIbRTojSgtLkVCZlG168xdF4cWvloM79AC646lYUD6WUwAcDa3GCtiz9nW+2NfMs5lp6PQYLTNKy414aWlsUjPN9jmfb35NE5nFEKrUWHmqPbw9dTYlmUVllTY9zdb4uVzu7T0w874LDw4oh30pWbM23QKQcVmDGlpsa1bdq/UdGHemawi+R4qe61lvtsaj3O5etx8ftXUPD1WH0mT90+mF+DFJYfQLcIfk/tH2Z6z5mgqDp/NrbKOLLDI5wR5a/HQyHZQq6rPBVgsFnyx8RSKSkyYMbJ9hXXP5hTj8w2n5Dr9YoJwba8IXKq4tAL5eoXRpiyEnC/xlxtPIU9vlGXwUFdd3qISI+b9cwIZBdbjl5qvty3LLipB4Pnb6uQUlSIcwNqjaeianIvurfzlfJPZgk/Xn5T3Q321SMm7sN2a/LonCUfO5WFgm2BsPJGOu4e2QZsQ7yrX/W1vEv6MM0EVlSrX/feQGLRt4YO6yiwwYM7ak+gXE4jxPVpWWi7Ox2X7z8r7IzqFYuOJDFzWOhATelZety7+2H8W+87kyPdEgJdHhWVGk1mWReehxH2Xt4VCoahxW+L8mbfptDy+YnuaGs7Hmoj3xSfrTsrt9YkWry202nXT8vSYu+4khrYPwZiuYWgKZrMFn6w/KfeVry+VnyUvjetiW743MRuHS/PQtdxzRNl/35uE2OQ8zLyyA/x1Fz5vxHn41p9HcOykCUnepzGkin2KOvxnbxJM+XqkK9X4YPVx3FpLOcX75oN/TkBfYpKPxedCdLAOh0QZRrVHap4BC7YnoEdmESLF8TWb8f7q4/D2UMvPEmocancc2UtMkZHitGo+p/Ms+HTfSeg0qkqB7Neb47F0/1l8szUBkwpLEBGgQyt/HcwqJV65vhvmnraupz3/RyjMz7NZy06OLdzfE/sBBPtogYyq1zmckgdTWj5KTWZMH94WOzcer7D8xWu7yiBj+YFzmL85Hv1aB6KNwRrUpOUb8Mm6U5CRCoAP18QhO996Dg47//ykrCKsOZqGq3uE4/lruuLVZYfx0h+HccegaCzenYT+MUEV/ujpjaaK+18aW+FxuxbeyCwske+JbkEKXBlWCNVFrym+XFD+295knEwvBHyAjIILAZcog/gjNr7EKJ+/KyEblhjgx/sGyaBHBM+vLT9cIZD9eE0c4k1F0PhVXZeeHip88M9xXNc7otrgSsg3GPHGn0flfRGotisXXC07cBYbTqSja0s/vL3yWKMEsot2nZGvr6W/Dr+eSMY0MdMCvLb8iFx+dfdw+UWhKtnFpXhn1XFMv6KtfPzvwTHYctJ6Mh1Nycdg+WWhuNp9n8kukqdHWr4ee7cn4r839pDzxeP/rThmW8+rjQGqOnx8iS/94rz7YqP1w098CXp8bKcq15391zGk5Fmw6ttd8rG3Vo0nx3VGXf1zNA1fbT4tj0dVgexXm07LQN1kscjzUVh3PK3Bgew7K4/Jc7dHK39M7NOqwrLErCK8t9r63rylf3SFYKwq4ste2fGd0KMlOoX7NqhMfx08J7+89o4KwOy/j9YYyP4dm4Kvt8TLBExTBbLiC5U4b6YNtwbz645nYGuHzApf2v7KSqkQyBrNlvPngl5+0bi63LHM1Zdi4a4z6BUI+dn0VxX7PJGWj7h0Fdp5A+L7Q21fIoQ9CTn4accZ3DrA+vnx4ZoT8vNG6BnpL7+wiHNmojIDt6qt76H3V5+wPf/pqzvLL96julRf34KIA8SXf/Fliypj0wIH0ypABz+dBkqlAmqlAncNjrEtE4/LB7RE5c8HL4+LQ73KRFasV1RApfkikHrm6i62wGx4xxboEl5NJFeNDqE+chvje5yPeAHMHNWhyi9enpdwDlf35yUySFen5w9uG4zB7YJlsDP18jb13r94jTVlYutKBLEXf6m9VOILyMQ+DQuKVUqFfG1i+r8xHeGpqf18ulhUkBdc0eiuYRjRqQVcWccwX9w5uDUcyVPjOsNHq0aIT8XMdUOIX5uuian5fRsZqEO4nydCfLT495C61UWwj4ftfVP2N7quRncJk1/Q/Mr9WlWV63u3wiOjG/aedAeMiIiIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickoMZImIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickpuN7LXokWL5FRcXP0oNURERETk+NwuIyuGp124cCF0urqNAkREREREjsntMrKOZFXCKhxIP4BSy8AK8y0WMz469gOKAnyAHbNhMFnHvCe6WLJPNjzqMCZ4vCkdW9Rx6GX2ws6kz1Dgk4OPvf3RwmgGzp9eaxLXIFn5B7ShxUjWj8FPxt3w0HrAWGxBqc8627bM3rvh4ZmPkoyRUKgK5bxic36V+/3nzF/I9lyH3xMDsKfAS57vQrYyB6u9dNjnoUXU0Z8BiKFxK76OfGMatKF/oFjZASuSUjG+7P0BS4V10w3x2J2zAnlmfa31cK50B3bnZyE280608WuDBcc/hSUoHj8cycBtnW+T46sXqmKh0pyrdVubzv2Dhaes49wLV0RdgQ1JGzAgfIB8nS08I6AJ3A2FsgQm81BsObsFG5M2Wl9DySAAvigxGVDkvQyzd+xAhE8E7uhyB/5Rm7E/MAD3GouRlXsaC48txLg249CrRS/Uhd5UiBzVetSfBT8e/RE5hhzc2+NeNKWV8SsxOGIwonytY9RfzKgT59gZlGSOAsxaOS9Ln4WvDn6Fy8Iuw8jokXKeqJsS3x1A/uWARQ2lNgU78zdi9o5AjIwaiQEtB9i2uej4IpzKOQWNSoP7etwHXw/fi169CZ/u/1TOLzsXKlEWQRuyDgZNBwAjKizakrwFm85uwr+7/hth3mHINeTiy4NfoltINyTmJcJL7YXbu9xuW/9M0VHM3vE9JnWchLYBbetUb2lFafg69mv0D+uPQ5mHkGcohCYoB6VZw2zrxGbEYtmpZfAyeGH/zv2wWCzoFdoL42LGyeVLTy7F6dzTsg68NF5IyEvA2vQvUWRph4UnV6JUDpc6vMJ+d6bslJ8Pd3S9A4XGbGhDlyFT64HZOzbb1jH5pQMKU62vYX7sfNzd7e5K8w8Y8zB7x2wMbDkQnf0GQBO0HvMPpcBsMaO5ZRRn4NujX2Fys++ZGoKBrB19sOcD+SHSRSU+qC+M+V5YWogFp5fjRvFAocSDvR5EjH8MpsEfnXPjcE10O6jaXC0/1IZnhACYbc+XQc3BooE+5Tp4KJfhyf6PYa9PBoK3rMRXHjnYoUyouKpFiWcHPQ2t5WVbwLe65BC2qpPwj3jHpx0HIoHP4A+VxQJkWJ/30d6PUAJfqHyS8WeG9Q+Uyd8Pw82A0Wsrnh34LF7f/jrMgX9AnLGm4hgotWlyvQxDYpXF/uTg+zAr20GBICgVSvQO7Y0unpOxLf4L/F9YgXWl7a8BeKNSIHuiaB08gjcjHZux/qzFFsjO6vMwPtjztW29fbl/4lDBX3WqxmJzNjKMcfjh8A+Y0HYCViUtARTd8OaON3FTh5vgqfZElmY5zEWhMOkjatzWN0fmoFdod4R7h8s/9N8f+V7O/+7wd7Z1PMOttylFUzDv6GcycMgszoTWmAd/3ITT+Ydh0G2EyTIRb+18CxPbT8QHWiPivfzQMS8Ox45nyu2K9/o7I96p02s8nrcL+ZqdqD8T3tgujgNkENhURoRPQpJ+L5bELcGMPjOqXKc0YCm0qjyYi6NhLOgm54lA6pvD32Drua22QPa1ba/B4meBMrMdzPpoaAK3Ib0kHSdzQnEy52SFQPb1ba/j+vbXY0P8BnQP7o6rYq6qsE+jKgVz9s2R98vOhYupfY/AI3gDCo1HATxSYdmnBz7F3rS9CPMKw7+7/Vt+qRFBZ3ni+JbZkv4bDuVav3A8NeCpOtXdX6f/kueXmHw0Phjf5lp4hi1Hac5ltnXE+SICWSHaGI2uwV3x3q73bIGsCBbzSvLkF4JhrYbhtxO/YU/uUrns6BlA4R9Q6bWJgFx8EfPT+uFYgRlq38MA+sj3dBmT3z9QKY3Vlj3IM0gGsO/ufhcT2kyosOza/EIEhkTiTEGyPAavDGwPz7C/8MFe6/u65fn1btVGIrdYhd+yhgKhtX9xbYg+8MStna9HUXpmk2yfGp/bNS1wFgEevngipwBP9H8C9/W8D2qlGgOhwxNZOQhXe6OFVws83v9xdG/R3d5FpWahQGn2EGhUHhgSMRiPXvYoupV6I7zIv8p1/9X5X9CKxGU9BVuGw1wcWXmLFh1u6nhTg0ruaxiF66Pvl+eymGK8e6NjqchoNcxdXe+sFPTWlZ8qGq08+tgeB3gEAdnWP/Dlleb2gcVYMWNXlVs73ypfkwgK6kIEz/3C+1WYpzB74+G+D8NdtPXpUefsckO09OgpM9hVEVnICO+av6A4C3+tP2b0mlXjOh0DO8rPgsZmMoQh2HCz7T0tJlgqB/7liaBXBPhViTAa8YSuHW7sINM31eqo9sEt5o4wG5ruGOqgxMw+M3FvtwebbB/UuBjIEhEREZFTYiBLRERERE6JgSwREREROSVe7GUv88SFBsmAuh7fJbxDKt6SWytQBaA/duMwrG05LUoVWiAHGngCH4p2oBeu7jcprVd+1yg3GY+kv4iV3p5YBi/b7BaKXIRYSoHPR4gGZDYRyIA/rL0WaEtzEVlyCvjvjQg3GvCXR6icf38R8EDhXIxa/Big+wVoc3mVu17v8X9IRyA6Ks5gcsmLALohuOhkrUV+TL0Q2UmbsN/3QnnLW6L+D1oqMpBqsraLDSxJxrCk/2/vTqCiuu89gH9nYxMFBURFwIAbLiguuG8xabBVkzYxm0Zf0zRpbaJ5ntIm5/hi7DO29kUTxZra9DVR+1IVY2o1sSfueyIuqAgqosi+IzAwwzJz3/n/RwaQAYyK5s58P+fMYWbu3O03v7nzmz//+78HkKGJAsKannBSL9CSi3cMf8IHmlHIVm6drdWagyuAC+uBTmLkBceC4qcDgf5Aynxg0Ax0NFViVeoT2GdtesJRE5d3A7U3bcvN/AZYHgQ8Mgka39nA2pFAdQXw2hHgyErg1N8Qa1VwpdNopFsScQa3+k5bHZx8c2w1cHwNqvWd5ElnQidUYZ/hPzGj0fsu9Lu2AZHZG3FTcbxv6yxLEeGehj2ahj6vYcbTwLLHgeBodM2/iN1uXphW84fmMxdeAv7blicY+GPgJ+txRwpSgI+nAlVFgI/SpLu0yJ/onL2AdxGgE0Nf5APrJwCmm0BwV2DrPMCaA/SYDPS6g3XtXIjVaZ9hf8cPcc2n6egy983NG8CyQCBqDhAtTtBsZOtcIHUvEDYZuLoXEHne2XHf7e57fglk7AeGO87ru/a3GCDi1qgIB/+IkRYrrroHw7+uAPhjOOAdKI81vh7luNlS25ixADAWAh+Nsw1QcjuvLkDi/wHlV4HqDHTa9XPb+3fLc1X/wGPbfw34BAEdJjWZtUbRoQYGdFjRE19ZPHDREIKxX1wB/qkB+j4BXP4KNcHTAEdDx3v4AuKcMb8+iMrchKEimQY1nJB3P4Vq8nHW/VWkKT3wTM277bIOV8UW2YclLwlQ2h6qpIkn1wFvnAGixMku5Op2dn0NWywNQwBZ9Z54rua/UCe+AUqu2epYALu8n4bJQ3zZtMFSjX3eM5CrdGnytK/GCA2sQN7FJs/7a8pggK1Q0lprEVCbC3QKQvFPtiBCmylvgBX9LanQWcxAcWqLqw7WFmKE9go6aUwI0eTL5zxFEdeG/ppMaFr5HPXX3ICfpgIaa6187GGphFa83lzW4jzuSjUMGou9SG9TQXKbn2VtbTlgvglYaoGqErjXlcFNqYGHUcSoBcZcoHuk7X55LlBjBPKToFXqgKIrQEWurZgruAhMeRtJ/k9gQNlh23Q7xXEhOOoXOBf2c/tTHTUmBGluDV/RiE9FKs50m4U1dY5PwumDG+ikqYIHauzPBYgRLOpMwPVD0FUVIULreEQLVBYB/aYB0/5g24c7ZbUA2aeA0vRm++cLI7Sw2OIl11EA1JqB+Sdsj3PP2uY32nKsTfnJ8n3yrbyGdlNVCtSZ5bqar/8iUFsJXP4SGPUqEDq2xcW4F6fYXlvjeCi8uybyrvZWFRgUhVz/0fCGCX7Wkob8K0iG1lGu1asqBsQwWuKz4siInwGT3wZyEuX7oy+50mRyN0uu7RhS0vzHbQW8MLn6A9S9dlz+cJ+gS8LlwbFAxAwg5V9A3xhcjraNxtHMlLeBBWeB+d/gs5GfY0X4RmDmWrSH7poSdNYYEaFpOsoM3Tu2yKqJmxfgF97kKY3B0OQvqV9r76lGb0Du4v9CUNwaWDQG9MqqxpxdddgTpUXWeMhWzY5WIPuEL6x1Gli0QJVWtODYvtx65Sl4+WsLtAqQEqLB5sm237LG91fgZ8drURaYjUkZJvxYZ8EXY7Wi2VUy1AK5Rzpj2eGGIsmj5jT8KmtRowd6pZoBvxRgiDssPiEQo3rlfuuL3xxWoLF0Qm10KUwJV1DyP8/Dd8IM+5HHUKdgwb+syK7sjKAxpdA2aoUJyKrCsgN1OBipRVr35qMUPJ1yGgFZRfB5TCOaFFtUkNgRyMqFVdt0GZqim/jl/2YjXfdPeJ+xIP+bNxG8chXGnbKgW4o3fqHNRZVSiqwJA4DeGnhWK1i4w4rspF9D69EwPFVZYhHGH3PH+clWnA+zxdPj1mvNbkDczDtvLyhdtgK/OlqHKi1gGKMF9A1N4HVmLXJ21sE3eLu9NbHwb5tRuTcL3RfpYNa1EoTbeXWBtbSh+HRk2Nn9qL6YjLrJQ+B9sxIBiUVYZY3D9R5ixImp8jUVZ9xQcdMPHc1VUDRaBB/fgxLDAIiGXUu1BtknOkPvYQWGNl++73Uj0v+dDp/poejshvaj1crj5qtfWVFV6AvPJwGvY2lIX/s8At5cCBh6ooupDG/u2ITMQAu8TbDnwu3vXFBZORbuq0NycDnwMzxYXn5ArW24u/bSsUrBgh1lyL+6BOgH/MceC5QsH3gH2X4Iinys1dneLHezCRkHu0DvaUWPUW3/6BTEceFXOy2oOBgLjxjbj7+Rl60Yse1zFEb2RucjHTFRpFdE28vyMBpxY78fFB8Nigb6yPe4LNEdFcV+qO7jjoosM4r3+KOL1gxrlIMRFXQ65K1YiZ6rV8Otix5lXiEoMhpt37ONWDWQx0vF3R2Fa+JQnZ6OykOH0fmll4B+bbfS9y3NwJMXDqOkVwf4PFKFghMdscoUh3I3L1jiTiP9SjKg0cB70kSUxsfDa4RtVBONXg83Sx3eSvg7lNopgGfT7wNT0kUUxa215bfV8Vi74jukdPNm+SPCeOgwdH5+cEZskVU5j8hIBP9lPUI2bXzYm0L3id+rP0fQhx8g8Dexzaf9+S+ozclBdaqtdTMqqwR9c4DhVxtaQ/SiQeqGF3qOK0WRf9ODX0SmAr0VONNbi3HJDfOYd3+F8AwFwxKuosBfh5veGkSlNUwXX+6V2R7YFa21344OCMa/J3jhl7/S4Ua4O/yviRayBmXpXjghRocz6mC+qUflueswnTuHTudPNVnuqMsKjFmesFQ3PRx1T6+S+zbyiuOWnnGZV+FeXAv/nNYPY2UZnsBTMbgxOKDJ8/rrWehz3YTHryZgzCUFVQcOoa6oCANTrajuWoejkZ1Q0NEToVfO2mJfDgxNA8q//BIG0bJ6S8Xlm+iSr8OQ6w3b6VcBDE9TMC5FgVvDS9tUtWs3zvfToqMJ8CjQNW0YK9ejMluB16kz9ufK9x+DKcsMU2o27re+V89Aya2EIaMIAUWl0FiBk90iMPp6w/tnuqFHVYE7AsrLkPv0XJT16oMO6Tm27TXqUZnnIfPAkc5XjTBllKHi2yQ8CBMvKLDkusOvBPA8ly1zsfK4aMUDepXnoX9OOh4/qzTJhdv1LSpGRBYw+nL7jGH6sHUvAYak16Jih21c2fEXFSDPDf65zfe3U1mp7f297vj9bcmkJAW1JxLQpdh2JZaoawp8UvNR9PkxuBXoMfDKnQ2t17GwSOaeOaOhPa7mhg5Vhe7QZOXBeKkIpmI3GC85Hg+255/WwlpZCfPF1v8bsORFA94Z/ywq//x3aH06oWhNnMwd46E7u+hIZFEaupWUoCLTA3UmHcx5BmzvPRHDCq/AGv8PdBg3VhayhavXwL1PHwTG2o77mh5BWDLmZUzKToRibt4voupUgtye7u+91+K6uy5aBI8BA+SyTYmJMO7bB2fEQlblxNVnvCdOhOdA26DhpH46b290iomBPqBp4SUY+vaD1tv7jpbjGeD4inDFHTVICm35yyKnqw7ZDn+4K/gmQmu/JYV2xaXebqjw0qCom+P/CJwX/0Bwu4sBbe+3vmHfaejZ6q4WJIV3wI0uLfd7bS+XwzQoe/CrbZPFU4vEgJbH/63z9kFFN8dX6iL6vnHv3Rs6X3Hxh9ZdC9TgVPdwKN16wBB4B33m2yCuIXE0aAgsty4m4RUdDUN32yUf3Hv1anLcT/Jr/Ypvhm7d4dYzqMXpej8/uIc1XGzJWbGQJSIiIiJVcrk+svHx8fJmMjk6hZGIiIiI1MLlWmRnzZqFrVu3wtOz0ThCRERERKQ6LlfIEhEREZFzYCFLRERERKrEQpaIiIiIVImFLBERERGpEgtZIiIiIlIlFrJEREREpEosZImIiIhIlVjIEhEREZEqsZAlIiIiIlViIUtEREREqsRCloiIiIhUiYUsEREREakSC1kiIiIiUiUWskRERESkSixkiYiIiEiV9HAx8fHx8mYymR72phARERHRPXC5FtlZs2Zh69at8PT0fNibQkRERET3wOUKWSIiIiJyDixkiYiIiEiVWMgSERERkSqxkCUiIiIiVWIhS0RERESqxEKWiIiIiFRJoyiKAhfk7u6OgICAB7Y+o9EIb2/vB7Y+NWBMGBPmCT83PJbw+MrvnIfHqILapLCwENXV1S1Od9lC9kHr2bMnsrKyHvZmfK8wJowJ84SfGx5LeHzld87D09MJahN2LSAiIiIiVWIhS0RERESqxEL2AVm0aNGDWpVqMCaMCfOEnxseS3h85XfOw7PICWoT9pElIiIiIlViiywRERERqRILWSIiIiJSJRay7Sw1NRVjx45F3759ER0djeTkZLiCXr16oX///hg6dKi8bdmyRT5fUFCAmJgY9OnTB4MGDcLRo0ft81RVVeGFF15A7969Zby2b98ONVuwYIGMg0ajQVJSkv35u42B1WrFG2+8gfDwcDl93bp1cJaYTJ48GWFhYfZ8+eCDD1wiJmazGU899ZTcL7HfIi/S09NdOk9ai4mr5onwgx/8AJGRkXK/J0yYgMTERJfOk5bi4co5Um/p0qVNjrFOnyNiHFlqP1OmTFE++eQTeT8+Pl4ZPXq0S4Q7NDRUuXDhQrPnf/rTnypLliyR90+ePKmEhIQotbW18vHSpUuVefPmyfvXrl1TAgMDlZKSEkWtDh06pGRmZjaLxd3GYMOGDcqjjz6q1NXVKcXFxXK5KSkpijPEZNKkScrOnTsdzuPMMTGZTMqXX36pWK1W+TguLk55/PHHXTpPWouJq+aJUFpaar//xRdfKFFRUS6dJy3Fw5VzRDh9+rQSExMj86D+GOvsOcJCth3l5+crPj4+9oQRB2aRJNevX1dctZDt0KGDUlBQYH88cuRI5cCBA/L+gAED5Ies3qxZs+w/ApwpFncbgx/+8IfK1q1b7dNiY2PtBye1+S6FrKvEREhISFDCw8PlfeZJ85gwT2w+/fRTZfjw4cwTB/Fw5Rwxm82ysUwUpI2Psc5+LGHXgnaUmZmJHj16QK/Xy8eiqT8kJAQZGRlwBbNnz8bgwYPxyiuvyEvMFRcXy39VNL40sPg3c308xN/Q0FCH05zFvcTA2eMTGxsr8+W5557DtWvX7M+7UkzWrFmDGTNmME8cxKSeK+fJ3LlzERwcjMWLF2PDhg0unye3x8PVc+Sdd97BnDlz8Mgjj7jUdw4L2XYmitfGXOWKwIcPH8a5c+dw5swZ+Pn5Yd68eXcUj8bTnTVW9xIDZ43Ppk2bkJKSgvPnz8v+btOnT3e5mCxfvlz2qX/vvffkY+ZJ85i4ep5s3LhRNpAsW7ZMFmuunieO4uGqOXLixAkkJCRg/vz5zaY5e46wkG1H4peiuIZxXV2dPQnEh060yjq7+n00GAx48803ceTIEVnQCqJ1tt6NGzfsrxV/60/quH2as7iXGDhzfMRnpf6g+frrr8tWFNGS4Coxef/99+VJFrt374aXlxfzxEFMBFfPk3qiYeDAgQP2x65+PKmPh8gFV82RQ4cO4dKlS7I1VrScitrjiSeewMmTJ50/Rx523wZnJ/rrND7Za9SoUYqzMxqNTTrir1y5UpkwYYK8LzqVN+50HhwcbO9DLJ5v3Om8a9eusoO52t3eH/RuYyDyaOrUqfaO96LDfnJysqL2mIh9z8vLs0/btm2b3Ld6zh4T8fkYNmxYsxMbXTlPHMXElfOkrKxMyc7Otj/evn27EhQUJM+7cMU8aSkeNTU1LpsjrR1jnT1HWMi2s0uXLsnO13369JGd0ZOSkhRnl5aWpgwdOlQZPHiwMmjQIGXmzJn2E9zEQUacgdy7d2/ZyfzgwYNNCuBnn31Wntgh4iUKfzWbP3++PLjqdDp5kl/9CSt3GwNxMBHLDAsLkzdxNrczxETss/hsiFyJjIyUZ8kmJia6REzECA6iPUFs+5AhQ+QtOjrapfOkpZi4cp5kZGTIE3Tq910UF2fPnnXZPGkpHq6cI60Vss6eI7xELRERERGpEvvIEhEREZEqsZAlIiIiIlViIUtEREREqsRCloiIiIhUiYUsEREREakSC1kiIiIiUiUWskREbRBXyunfvz+GDh1qvyUnJ6smbp9++imeeeYZfB98n7aFiNRP/7A3gIhIDbZt24ZBgwa1y7LFZaz1eh6OiYi+K7bIEhHdA3FN9xUrVmDUqFHyOueffPKJfVpqaip+9KMfYeTIkRgyZAjWrVvXZL6VK1di8uTJePvtt5GdnY2pU6di4MCBmD59urytXbsWOTk5CAwMRFVVlX3eF154AR999FGzbampqUFsbCwGDx4s1xcTE2OfVlFRIecT00aMGCGvQS/k5eVhypQpGD58uFz3ggULxBUf5bR3330XL774ImbMmIEBAwbg0UcfRUlJib1lVVzL3dEyhU2bNsmYDBs2DJMmTUJSUhLzjIjuOzYBEBHdAfHvcA8PD/vjkydPws3NTd4Xz3/77bdISUlBdHQ0XnrpJVmoiiJQFHSiW4IoREePHi1vorgTqqurcfDgQXn/6aeflgXl4sWLkZGRIVt/RSHao0cPPPbYY/jss8/wyiuvyMJz7969+Pjjj5tt4+9//3ukpaXh1KlTcHd3R2FhoX2a2L5z584hNDQUb731liy+169fD19fX+zcuRPe3t6wWCx48skn8fnnn9v//S/mS0hIQJcuXfD888/LeUTh3doyjx07hs2bN+Pw4cNyO44cOYLZs2fL1xIR3U8sZImI7rFrgSjShIiICNlFQBSb5eXluHjxoiz+GreKir619YXsyy+/bJ924MABrFmzRt4PCQmRrbP1Fi5ciNdee00WsqJQFAWyKDxvt2vXLtnKK4pHISAgwD5t/PjxsuAUxowZg7i4OHnfarXit7/9LY4ePSpbYgsKCmQf4PpCdtq0abKIrZ/vwoULbS5zx44dsmgVLbL1RFEtWoyJiO4nFrJERPeocUutTqeTfV5FUejv74/ExMQW57u9GBWtuI6IVl6xjkOHDsmW2P3799+XbRRWrVqF4uJi2boqXrNo0SKYzeY252ttmth3UaT/7ne/+87bSUT0XbCPLBFRO+jXrx+8vLywceNG+3NXr1619zG9negrK/qdCpmZmc2KVdEqO2fOHNmPtW/fvg6XMXPmTHz44Yeyy4LQuGtBS0pLS9GtWzdZlObn5yM+Ph73SvSpFfst9qO+1Vd0dyAiut9YyBIR3QHxr/bGw2+Jfp+tEV0MRN/TrVu3IjIyUhagomuAyWRy+PrVq1djz5498iQt0So6btw4+Pj4NFm/0WjE66+/3uI6RReB8PBwREVFyW2cN29em/slTu46fvy4fL1oRRX9ce/VxIkTsXz5ctnfVuyP6JKxZcuWe14uEdHtNEr96alERPTQiALXYDDIAjg3N1eOdLBv3z7Zslt/cplokb106RK0WrZBEBEJ7CNLRPQ9IIbqmjt3ruxfWltbiyVLltiLWNGS+/XXX+Ovf/0ri1giokbYIktEREREqsT/TxERERGRKrGQJSIiIiJVYiFLRERERKrEQpaIiIiIVImFLBERERGpEgtZIiIiIoIa/T+CdgMlyKSqRAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times_ch1_4 = [170.448, 230.292, 210.383, 238.595]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.14', 'Ch.10', 'Ch.6', 'Ch.2']\n", + "for idx,s in enumerate(single_ch_spectra):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Current comparator background spectra, SiPM-Channels 1-4')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "aee0452b-0a8b-4f2f-83e7-94bed99372dd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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CWQKTzTMZnoJMAGlOlnm7dDFZOrNNebNrJ8K0VcxbN6b9bf369a3BtC81mSYTlJqsosnImKDmeEw5TWbq6quvturPZGdMu7+8/7TNP6e8dWK+pJh/tnm30bS3NP+8T2QbTWYrL7NdJsBxlc90q2SuUjdXOJssdnx8fL59YaYvqcsek+k2gZ0J6t5+++3c90121hyHJuvoUlQWsyCzjWb/582+mjai5lh2BX0Fj2uTxSoLs/1F7e/jMes3mVkTFJljwWTli6tr89zsR7M/TXbUBHamfWvBz45rm0z9F9cNmyvgON5npzT79ESY9bdp06bEXgtcAajJrhcMYsvjwQcftII6cwyaNtvminyTIc6b2TV9qpq2oaZ+TX0WPDeZL6FmftPGvSATjJa0XQXr9oYbbtA555xjZahd59mKVN7zrGnTbs6TxbWbr4wyu5gvTKZdcF7mtenmDJ6NQBZu8eabb1r/iE03K+ZCEfPTk2lUby60cvWtaC76MNkyc7GDCYZMtuJEu+gxP6mZoMRcMGF+YjTrnjJlSqELGUrSsmVLaz5TFvNTcVHf/k2AbrJ85h+xuZDJrNMEDq6gw/Qr6bpwxlx4ZS5SMMt1/dR4okzmylzwZrbt77//ti7cMdtsmGDEBD7mH6jJwpptLk3gY+YzP4WaTJUpqwloC2aBzQVv5sI9k8kw2Rjzk/d1111nXShk5jNBtNlGcwGI+edZVuYna3NxmumezXTBY4JCV3bLlM8EoWYdZjDlM/XgYrrRMZk589OuCULNMkx2zOy7vMz+Mhe0mYys6arJFeCarKU5XszFd6ZOXf9cS8qGmn/EJttlttmU3VxcZfZF3gunzHFtLowz481FcWU5Bg1Tj2YZrmPZBEIlXTBnmOYT5gIsE2SbwNl85lxfCFzMxWzmM2gGc5ya+jMZWPMz9O23324dXyZzbb4Emf1gmgC4un4yF5uZi9HMesw6TCBsLoIzzBcgExyadZoMV0lfLI63T4/HZPLMPjABowmoTFlMmcznzVwEVdVMd1nmM2GOO1fzAVOvJkh3DebLovmyYH4BKYo5nsxnwOwTExSbz645X5p+Zc0xaraxNEzdmuYlpl7MxV55f4GoKOU9z5q6euutt6zsuTnmzPnHdIlmjh1zUWDBQLM45gJT86Uy75cz8yXcfHEwXyJN8O/6IuFiLiY1dWvOM+vXr7c+s+a8WfBCVXggdzfShffaunWr1bWS6WbKdCNkuoW64447ci/MMN2fPPDAA9aFF+Hh4VY3KEVd7FXwwoGiLhIzzMVRI0aMsC7GMBeD9OvXL/fCpdJchGMu+jIXXZmLSI7X/dall15qXcRhph08eLC1rUV1v2W6gyqq+628Cm5zwbK6LlYxj02bNrUuejAX9uS9YMxctGHWabbbdI1luoTKe+FaUeswF6CY7TDb0KRJE6v+Tdlc3ScZptsnc5GJr69vbvdbpp7M+s1FG8V1v1XU/inIlMl0X2S66jLbZLoBy9vFTkxMjGPkyJHWhTWmmyozzlUPLmbfjRo1yprf7I+zzjort1upgtv8+++/W9NNnTrVem26JjIXMZljs0ePHo6PP/7YqndzkUzeei/I7E/TrVpx3W+Zi2vMhXWuLs1++OGHIrvfyqvgceFahrmw0HR5Zba9pGPSXKjWvXt3q67q1avniIyMzN0O10U+5gIkc1GO2d6C3S6ZC/PMRVOmngMCAqx9ceWVV+Yuw/j888+tLp9c480x52K6ezOfc3PxU97ut8xFiXmVZp+a+fIegwWPWfP5MhdHmWPdDKb7K7P9Zek2K6/ydL9lvPLKK9bn0mxXcce9OQ67du3qKIk5Ls25xHwezbFsui98//33c9dfVH3mPU+Y48t0L2WOS9O91vTp0/NdrFlwe4rapqLOEwXXW9bzbFFMWcxn1XUOMfvTdC3munDzeOVwnbvzXohqzjsFu4IrGAKZi8XMMWw+V71793bMnz+/xHLCM/iYP+4OpgGcOJNpNXcd89Q7olUXJmNmspLm583StPezC9MkwPySYNpKHq8NpruZbrjMBXYmw2rKDACefdYCADcx7RZNG0TzE6X5Odb8lG5+Fq5OQazdmOYq5qd4glgALgSyAFBM9s+0IzRt9MwFWqbHhhdeeIG6ciPT1toMAOBC0wIAAADYEr+RAQAAwJYIZAEAAGBLXttG1nS0bdq9VRXTGXbe246COuE44bPDuYTzK/9zqg7/h+1ZJ6b/aVPO4nhtIGuC2Krsrmj69Onl7vS+uqFOqBOOEz43nEs4v/I/x32m2yA2iYiIKHE8TQsAAABgSwSyAAAAsCWvbVoAAAC8Q05OjvWYnZ3t7qJ4nGw314mPj0+5bjRDIAsAAKptAGtuamJucGKujdm0aZO7i+RRGnpInQQHB6tly5YnFNASyAIAgGopJibGCo7at2+vpKQk1apVy91F8iiHDx92e504HA7t2bPH2leNGzcu8/wEsgAAoNoxAVJCQoJatWolf39/K6D18/Nzd7E8iq+H1EmjRo20Y8cO69E0NSgLLvYCAADVMpA1Q0BAgLuLguMw+8i1v8qKjCwAAKh2iguK0jKzlZntvPirIgX4+So4wP3ZTTsjkAUAACiGCWJP/e8MHUwq/k5RJ6phWJBmP3A6wWwVIyMLAAC8gsnEmiB2/kNnqGZQxYVAR9KzdMrz/1jLL01Wdtq0aXr22Wetrq/M7VebNm2qP//8U+eee67eeOMNtW3bVtdcc43++usvNWjQwJrmtNNO05tvvmn9DG/a/aakpFgXSbmaTvzzzz8aNmyY7r33Xr388stFrnfr1q168MEHtWTJEtWsWdNqI3vHHXfohhtusNbXt29f3X777cct/4QJE/Tjjz9aPUKsXr1a3bp1O+48n3zyibWOn376ydrOikIbWQAA4FVMEBsWHFBhQ1mC4v379+vmm2+2gtkVK1Zo/fr1eumll6yLnH799VcriHUxQaeZZvny5Vq1apXeeeed3HEtWrSwgkmXDz/80ApES1rv4MGDddZZZ2n79u1WAPrDDz8oKyurzPU3ZswYzZkzx+oyqzSio6M1efJknXzyyapoBLIAAABVZN++fVYvCvXr189976STTrICWZNpXbNmTZH9rJ566qnauHFj7nvXXXedFbwaiYmJWrBggUaOHFnseidNmmQt48Ybb8x9r27dulZQ7bJu3ToNHz5cHTp00MUXX6yMjIwil2WywxEREaXe5ptuukmvvfaagoKCVNEIZAEAAKpIz549dcopp1gZ1YsuusjKxpomAiWJj4/X9OnT1adPn3zB5LZt26x5v/rqK0VGRpbYldbSpUut9ZbEZH/NT/8mS3zgwAFNnTpV5fX222+ra9euGjBggCoDgSwAAEAVMe1STYA4b948K4M6d+5cK9DbsmVLoWlfeOEF9e7d22r7Onr0aKuNaV7jxo2z2p6azKzJ0JaXycKGhIRYAXH//v2tNrXlYZowvPfee3rqqadUWbjYCwAAoIp16tTJGsaPH28FtHnbu+ZtI1vSxVfXXHON1SzBNAUwdy8r2I7VFRz//fffVjZ3/vz5uvvuu4tdnmnC4GKC2bK2nzUXp913333Wc5Mhbt26tfbu3avOnTvnttO9/vrr9cwzz+Rr4lAeBLIAAMCrmF4G3LU80xTA3MVq0KBBuc0GTOYy70VepdW0aVM9//zzVkBc0Lfffpvv9a233qpevXrpo48+0rXXXpu7bvP6rrvuUkUw7WtN84S8Lr/88tznQ4cOtQLdiuy1wOsC2aioKGtITU11d1EAAEAVMjctMP29mq6yKppZrln+8Zgsp/mp3QSvoaGh1uurr75aF1xwge68884yr/fao0Hp8TRp0sTqacBkec36w8LCrGYOpiut4zHZYjO8//771uvbbrvN6vHAZFhN8Gq68iqqaURV8HGcyG0UqgFztZ3pDqKqmEbaI0aMqLL12QF1Qp1wnPC54VzC+bWymD5aN23aZP3sbn4mN1f2165dmzt75eGqE0/bV2WJ17wuIwsAALyXuWEBt5KtPui1AAAAALZEIAsAAABbIpAFAACALRHIAgAAwJa42AsAAHiPzDQpO6Pil+sXKAUcu6EAqgaBLAAA8J4g9vUe0pEDFb/smo2kO1eVKpidNm2ann32WavbqfT0dOvGBn/++ad1o4A33njDujmCuWuXuVNWgwYNrGlOO+00vfnmmwoICFCrVq2UkpJi3VzBvDb++ecf61a29957r15++eUi12tuOWv6kV2yZInV96vpR/aOO+7QDTfcYK2vb9++Jd5JzMWs39wFzHUnsIceekiXXHJJkdOadZl1pKWlWYPp9/aBBx5QRSGQ9SbJsdLU66XaEdIFb7q7NAAAVC2TiTVB7N3rpKCwiltuepL0Whfn8o8TyJqbCNx8881avHixWrZsab23bNky+fj46Ndffy3yFrUmADz99NP1zjvvWEGh0aJFC+smBaNHj7Zef/jhh1YgWtJ6Bw8ebN0MwdwYyjB3GPv9999PaJPNncO6det23OnMrWiffPJJnX/++YqLi7PuQmYC9i5duqgi0EbWm+ycK22bIa386th7896QPh8txW51Z8kAAKg6JogNrlVxQxmC4n379snf31/169fPfe+kk06yAlmT6VyzZk2heUzm89RTT9XGjRtz37vuuuus4NV1Y4MFCxZo5MiRxa530qRJ1jJMYOlSt25dK6h2WbdunXWnLnNjgosvvlgZGRXTBCMhIcF6TE5OVmBgoOrVq6eKQiDrLbbPlqZcJTUscD/m5V9IW/5yBrkAAKBS9ezZU6eccoqVUb3ooov00ksvWU0EShIfH2/dDbNPnz6575mmBtu2bbPm/eqrrxQZGVnorlh5LV261FpvSVasWKGffvpJ69ev14EDBzR16tRip73iiivUvXt3q1nCwYMHi53uo48+0qOPPmptrwmQn3/+eTVu3FgVhUDWW6Qckpr1kS7/xt0lAQDAa5l2qSZAnDdvnpVBnTt3rrp27aotW7YUmvaFF15Q7969rbavpgmBacea17hx4/TJJ59YmVmToS0vk4UNCQmxAuL+/ftbbWqLMmvWLK1cudJqEmEyy1dffXWxyzSBuhl27dqltWvX6uGHH86XWS4vAllvycb+eKcUnOd+yjvnS6u/lTJT3FkyAAC8kmkrOn78eH3//fc6+eSTrfauBZk2ssuXL7cCxscee8xqfpDXNddco4kTJ1pND9q3b59v3JgxY9SrVy9riI2NtbK58+fPL7FMrou3DBPMZmVlFTmdya4a5kKzu+66S7Nnz7Zem4vTXOs0F7MdOnRI3333ncaOHWuNb9OmjQYMGGAF8RWFi728wYG1UpMe0pgPpLREKSdL+niUFFxH8g+W6jgbmwMA4BXMxVluWp5pCmAusho0aFBus4Ht27dbPRWUVdOmTa2f6k1QXNTFWHndeuutVoBpfuo3PQe41m1em2C0tEw718zMTNWpU8d6bZo1mKyxYdrXmuYJLqZXBhMcz5w5U0OGDLECW9OWl14LUHYmGxtS1xnIGo4c6fbFUo0G0mcXSwvflZr0cga8AABUR6avV9NNlulhoKKZ5ZrlH4fJcpqeA0zwGhoaar02P81fcMEFuvPOO8u82muPBqXH06RJE82ZM8fK8pr1h4WFWc0cJkyYcNx5TbbYDO+//77VdtY0czBBqsPhsLKsn376aZHzmazulClTdM8991jbaQLg++67T/369VNFISPrbUz29dz/SX4BUujRKyZHPi99f6u0aTqBLACg+jJdY5m+Xt14QwTT5Za5cKsoJlPr8vHHHxe7jLzT5fXEE0+oJKb5Qd4LuExvB7Vr1y5yfXn7ojVdZ5nBMIGrae5QWiZLay40qywEst7GtK/pW+DbW8OOUoMO7ioRAABVxwSb3IGr2uBiLwAAANgSgSwAAABsiUAWAABUO66uqswFSfBsrn1UsHux0qCNLI6JXiQl7pFqN6NWAAC2Zq7IN/2cmj5UTaf9OTk51pX2OMYT6sQEsWYfmX1l9llZEcjCqfsY6bcHpE/PlxJ2Sd0jpQvfonYAALZlOu43d5SKi4tTamqqddcqHOMpdWKCWNdNFsqKQBZO7YZJnc+X5rzqfB2znpoBANhaYGCg2rVrZ2Ue//zzT6srKBxj7sTl7joxzQlOJBPrQiBbjX21aJe+nD5bT/t9rWbhDdXQ3QUCAMANXIGS6aAf+dm9TrjYqxpbvD1Ot9acpYaOOH2ac5a7iwMAAFChvC4jGxUVZQ2mXYg3CK8VpMO1TtamQOd9kEvUtJdUo6FUO6IqigYAAFAuXpeRjYyMtO776wmNmz1Olwuk+7dIg+9xd0kAAACOy+sCWQAAAFQPBLIAAACwJQJZAAAA2BKBLAAAAGyJQNaLxCSl6cJJc3XbF8vcXRQAAIBy87rut7zZrtgUrdidoDV7Eo8/ccYR6fA+qVaTqigaAABAmZGRRWENO0rpR6RPL6B2AACAxyKQ9UIOSc/9ul4PfLtSKRlZRQeyYz+R0pPcUTwAAIBSoWmBF/Dz9dHcLbFKyci2XmfnOPTurG3W82sGtlaXprWKmdMh5eSYm1RXYWkBAABKhwjFC7RrWFPXDGyl2ZsPlX6mkHpSSqw0qV9lFg0AAOCEEch6AV8fH3Vu4sy6hocFacKw9rr3zA6qFeyvHIdDmdk5hWdq0E66cYYUu7XqCwwAAFAKBLJext/XR/ec2UF3DGsvX18f3fz5UnV45DfN21JEtja4uCYHAAAA7kcbWS8xsG19XXlyC3VvVjvf+9Hxqdbj7vgUN5UMAADgxBDIeom6NQL1zIXd873XuFawktKyVDskwG3lAgAAOFEEsl7s+9sGKT0zR7d/xZ2+AACA/RDIerHgAD9rAAAAsCMu9kIJfJx9yf7xiJRdxI0TAAAA3IhAFsWrHSFdMEma94aUGkdNAQAAj0Igi+L5+Eg9LnE+X/ud5DA3twUAAPAMBLI4zhHiLw26U5r+sLR/NbUFAAA8BoEsLD+s2KvdcSlFZ2XPfOrozRHIyAIAAM9BIAvdcUZ7HTqSru+X76E2AACAbRDIQv1b11P3ZnWoCQAAYCsEsgAAALAlAlkAAADYEoEsSnmkBEi/3Csd3kuNAQAAj0Agi9K5Iko6vI8uuAAAgMcgkEXpNOkhhXBBGAAA8BwEstVUcnqWDqdllnp6Xx/p+xV7NH9rbKWWCwAAoKIQyFZTD0xdpdmbD6l+jcBSTX/n8PZqWidEv6ymDSwAALAHAtlqKiElQ89c2E2t6tco1fQRdUPVvVntSi8XAABARSGQRb670UYtiVbHR37Tkz+tpWYAAIBH83d3AeA5rj6llWoGBWhvQqqW7Yx3d3EAAABKREYWucJrBeuWoW01qF39omvFL1Ca/n/SAbK1AADA/QhkUXqRH0kBIVL0YmoNAAC4HYEsSq9uK6lGODUGAAA8AoEsAAAAbIlAFgAAALZEIAsAAABbIpAFAACALVWLQDY+Pl7XX3+9mjdv7u6iAAAAwFsD2QkTJqhVq1by8fHRmjVr8o3bvHmzBg4cqA4dOqh///5at26d9X7dunX1wQcfqGPHjm4qdfWzOz5VXy/aVfTI1d9KcdurukgAAACeHciOGTNGc+bMUcuWLQuNGz9+vG666SZt2rRJDzzwgJWFRcUb0iFco7o31v/+2lx45Gn3Symx0qpvqHoAAOBWPg6HwyEPZLKyP//8s7p162a9jomJsTKxhw4dkr+/v0yxmzRpogULFljTGsOHD9dff/1V5PJeffVVa3BJSEjQ1KlTq2hrpLS0NAUHB1fZ+l5bka17gn/SmfFfaWvEGG1tHlmm+bcmOvTu2my9OLDwXYy7bnlLYSm7tbbteCXVcNa9HerEDqgT6oRjhM8N5xLOr/zPOcYkLaOjo1Ush4dq2bKlY/Xq1bmvlyxZ4ujcuXO+afr16+eYOXOm9Xz8+PGOZs2aWY9btmw57vLNtFXp999/r9L1Xf7efMf2d69wOH7/P4cjK7PM8y/ZEecY8OxfRY/ct9rheG+Yw/HjnbaqEzugTqgTjhE+N5xLOL/yP6f08VrhdJsHM+1m88qbTH7nnXfcUCIbCKkr+VXwbm7cTeo4Spr9ilSvjTRoQsUuHwAAwI5tZItjeiQwqeWsrKzcIHb37t1q0aKFu4vmnU65XTrpamnnPHeXBAAAeCnbBLLh4eHq3bu3Pv/8c+u1ad9q2sa62seiYgX5++rQkXTd9uWyoifwD5TqtabaAQCA23hcIHvbbbcpIiLCyr6ai7fatWuXO27y5MnWYC76euGFF6wut1A5ujWrrTcu661fV++jigEAgEfyuDaykyZNsoaimH5i58+fX+Vl8lbdI2q7uwgAAAD2CWQrW1RUlDWkpqa6uygAAACoTk0LKltkZKSmTJmikJAQdxcFAAAA5eB1gSwAAACqBwJZlMh01fv+7G367+8blJ6VnX9kjQbSpt+l72+lFgEAQJXzujayKLtnfllvPY7q3sTqzSBX14uk1Hhp6SdUKwAAqHJkZFGsQD9fmZup+fn6yDf/TdWOqRHufExNkDKSqU0AAFBlyMiiWOG1gvXT7YOtYHbcB4uKnzB+u/RyBym0vnSvM3sLAABQ2bwukKX7rbLJ15SgOGmJzsekvSe0TwAAAE6E1zUtoPutE5eSUeBiLwAAADfyukAWJ6Z5vVBd9t4CrdlzNPsKAADgZgSyKJWpN5+idg1rKiYpjRoDAAAegUAWpeJ/tAcDAAAAT0Egi/Kp20oKDJMadnK+/udZKYe2tAAAoPIRyKJMpi7do/jkjGNvNO4m/V+0dOsCadTL0qz/SkdiqFUAAFDpCGRRavec2UErdifo97X7C4807Q76XkdtAgCAKkMgi1I7q2tjtQ2vSY0BAACP4OuNN0QYO3asUlNTZUtrpkkfjpQ2/yWPtWeptCqKtrIAAKBSeV0ga/sbImz8Tdo1X9r8hzzWN1dI026Qtvzt7pIAAIBqzOsCWVQhB70XAACAykMgi8oz4znp4CZqGAAAVAoCWZTZDyv2aGdsctEj/QKdj2M+dLaR3TmHGgYAAJWCQBZlcuewdopPztR3y/cUcTT5STf8JV39s9RttFS7GbULAAAqjX/lLRrVUZ+W9dQjorYOHE5TWma2ggP88k/QpKe7igYAALwMGVmUWY/mdfTVot3q9OjvevH3DdQgAABwCzKyKLNxJ7fUFf1b6I1/tmj1noSSJza3q83JkXz5zgQAACoW0QVO7MDx9VG9GgElT9SomzTzv9Li96hlAABQ4fy98c5eZrDtnb3sZPjjUvJBKSXW3SUBAADVkNdlZG1/Zy+78fWXVn4lbZru7pIAAIBqxusCWVSx0//P2cRgw89UPQAAqFAEsqhcNcOdgWzaYSmT5hwAAKDiEMiiXKLjU7Vxf1LJE4V3dmZkf7mX2gYAABXG6y72QsU5pW0DfbNkt8Z9sFCZ2TlqGBakP+4eUnjCbhdLqfG0kwUAABWKjCxOWLvwmrrjjPaKSUpXfEqmNh04UvIMh/dKK76S9i6n1gEAQLkRyKJqtBoshdaTFr4jfXW59VZ47EJp8fvOGyYAAACUEU0LUDUadpSu/lHas0z68hLJ4VDvTa9ImyQ16S1F9GFPAACAMiEjCwAAAFsikIUbOKyMLAAAQHkQyKJcIuqGKNDfV63qh5ZuhpC6Ulqi9MZJ1DwAACgXrwtko6KiNHbsWKWm0jl/RejatLY2PXO2vrzx5NLNUK+1dMt8KX57hawfAAB4L68LZCMjIzVlyhSFhIS4uyjeq0E76ZpftajrE1LNRu4uDQAAsCl6LYB7tBqk+I3H6XcWAACgBF6XkQUAAED1QCALAAAAWyKQRYUI8POVr4902bsLlJaZTa0CAIBKRyBrM8kZWdZjSqbz0VM0DAvSj7cP1vxtsUpMzXR3cQAAgBcgkLWZDfuSrMfV0YnyNJ2b1Mp9vj8xTXO3HFJODjc+AAAAlYNeC2zGYe6K5eHe/GeLPluw03o+pk+EHh7VWXVrBBY/w+E9kvpUXQEBAEC1QEYWFXcw+UhPX9jNerz6lJZ64eLumr81Vh/P21H8TE1PkqKulnbOk+J3SHP+J8VtY68AAIDjIiOLCuPj46NxJ7fM997yXQkl55Av/1qafJqUGi+tmiIt/UiK3SJd8CZ7BgAAlIiMLDyIK+T1/OYTAADA/cjIwv1C60vfXi/VbubukgAAABshI4sqkZ3j0D8bDig6PqXwyLGfShF9nU0KAAAASsnrAtmoqCiNHTtWqamp7i6KV5m2LFrXfbxEE75aXnhkUJgUWMMdxQIAADbmdYFsZGSkpkyZopCQEHcXxSv4+flYQezva/Zbr9OzcqxHh8OhH7Zl68Gpq5ScXuDmDrsXS9tmuqO4AADARrwukEXVunt4B3VqHKa/N8QUGvfrToe+Xrxbm2OOHHuz52VS7Qhp3sSqLSgAALAdAllU+q1ru+S541fxR+LR6w7rtpY6n8deAQAAx0UgC88w8gU5LnpXaX3H0/kWAAAoFQJZVLpB7RropBZ1NLxzePET1Wmud+L7qtMzc/XvxoPsFQAAcFwEsqh0A9rU17RbB+mSfi1y3zucVuACL0m74pxdc8Ulp7NXAADAcRHIokodTErXL6v2afCL/6hWoBQcUMwhmJYoJR9i7wAAgGIRyKLK9G1ZVz2b19FtXy5TUlqWXjjFT2HBAYWm2xvcTorbLn11qZSRzB4CAABFIpBFlalbI1CPntNFgf6+Cg8Lko9P0dPtDu0iXfW9FL1Yeq6pFj17pnKys9lTAAAgHwLZamjR9jhFx3vmncta1A/V4oeHa8Z9Q+VbXCRrNO6uNddv19UZ/1H/zEXKySGQBQAA+R3tvBPVycPfrVbzuqFqFBYsT1Q7pHBzgiL5+mldTsvKLg4AALApAtlqyCHpxtPaKGSNn+xo44EjGvP2PDWoGeTuogAAAA9GIAv3HoC+Pnrk+9WaeGnv3PdW7k7Ifd7QTeUCAACejzaycKv3ruqr/YlpOuOVmfpq0a5ip/PZNF1a+omUk1Ol5QMAAJ6LjCzcqluz2pr/0DB9v3yP/lh3QObyL/NoejbIyMqRv5/zgjC/qCudMzTqKkX0dW+hAQCARyCQhdsF+Pkqsm9zaziclmndMOGUNvWtO32FZsRK3+aZ2GFaAOcRs0H6+S6p7TBpyP1VXXQAAOBGBLLwKLWCA3RZf+etbFs1qKFD+4/TjdjuhdKu+c47gRHIAgDgVbyujWxUVJTGjh2r1FTP7GcVx7F7gTT7VWfgCgAAvJrXZWQjIyOtISIiwt1FQSnkhNTXW1nnq0ujGuqb+Jtq/vGIc8TeZdI5r1GHAAB4Ma/LyMJeGtYKVfDIp7Ss493akVlPDnM52KA7pf2rpYXvuLt4AADAjbwuIwt78fHx0XWDW1vPhy5+ShNHd1CPTh2l9KSjt34AAADeiowsbCPVJ0RZoeHuLgYAAPAQBLIAAACwJQJZAAAA2BKBLGzl8/k7lZSW6e5iAAAAD0AgC9t45JwumrX5oOZuOSQFhUkLJ0tLP3Z3sQAAgJsQyMI2zuvZVM3qhOjDuTsU3fNOqcdYZ3+yAADAKxHIwlYeO6+r4pIz9NGiA8o84wnp/DelkS9KqQlS9FJ3Fw8AAFQhAlnYSp+WdRXZJ0JfLdql9xYdlE4aJ3W9SKrXWvrqUncXDwAAVCEC2WoqYv370qbfJP9gVTfjh7TVRb2bKTk9y/lGWCNp5AtSztHXs16Spt4ofX+blHzIrWUFAACVh0C2mqq3d5bU6wqp/03yOjOel2o1kbbNkL67WTq8190lAgAAlYBAtjpr1E3yD5TXyEyVVn7jfN73eumCSVLcVmndj+4uGQAAqAT+lbFQoMo16CD1vlL6Lk8Guu3pUngXdgYAANUUGVnYUnCAn75etFvfLY92vhEQLA35T8kz7V8tTRogTX+4fCuPXiLNflVKjS/fcgAAQLkQyMKW7hreXqd1aKi5W2KPP/GhTdLnY6R3BkuJ0dKWv8q3chMI//2ktPyL8i0HAACUC4EsbCksOECtG9Qo3cRLPpCOHHD2OXv+xMouGgAAqCIEsvAO7YY5+5wNqu3ukgAAgApCIAtbiz2SrsTUTOeLoJpSk17O3hpC6hY/k8Mh5eRUTAF2LXA2WZhLphcAgKpGIAvb6h5RW0t3xuuhaaucbwSESONnSrfMlYJrOd8b8oA05EGp3w3O1zUaSLGbpQ+GV0whds13XkS2+Y+KWR4AACg1AlnY1ukdw/V/ozrrUFKGUjKO3tWroCY9pdMfkmpHOF837SVd84sUu8V50dfG36q0zAAAoOIQyMLWWjWooeW743Xjp0tKP5MJatMSpc9HS19dKj3VQIrfIe1eJP3+kHRoS9kLErtVeqGF9MFZZZ8XAACcEAJZ2NrJberr3XF9raxsqdVpId27Ubp/m/TAdmdzg8P7pHkTpQVvScs+KX7ezDQp52ib3LyS9jqD4wNrT2xDAABAmXFnL3insMbHnvsGOJsamIvAjKR9zou4vr9VanuGdM7LzvdnvST984zzea1m0p4l0qHNbig8AAAwyMgCrQZLP98tbf3H2evBxt+lzy6W4rZK22ceq5+ZL0lDH5L+b58U0U9a+50UVEs67f5j02QkO3sxMPOnJ1G3AABUIq8LZKOiojR27Filpqa6uyjwFBe9LTXtLWWmSD0ukU69W8pMLnra7pFSYOix181Okuq3cz6v21oa9pjUbri0fZaUtL9qyg8AgJfyukA2MjJSU6ZMUUhIiLuLgurCL9D5GBAqnXqPNPwJyZdWOwAAVDb+2wJGUNixx6wyZuu7XChdFio1aJ///bjt0t4V0sZfpNMfLjweAACUC4EsYIx+X4rbJjU9SZr/xtE68TlWN/tWSY7souvK11fqODL/e60GObv28g92NlMwyyWQBQCgQhHIAkZoPedgmLuA1Qh3tpld+I6Udlh673Sp+QCpZnjp6uvKqdLH50o75kg18/SQAAAAKgyBLFBQYA2p12XOXgyMnCzncEWUc5wx6E6pbkvppKuLr78rpzmbKfx0F3UMAEAlIJAFjufw3sLvmd4KzFDipyvQOQAAgErhdb0WAGVyaJP0ziCpcfdjvRMAAACPQEYWKI65OUK3MVLDjtKQB6gnAAA8DIEsUBxz8deYD6gfAAA8FE0LUC1kOxzKzM5xdzEAAEAVIpCF7TUMC9KWmCNq//BvWrgt1t3FAQAAVYRAFrbXrVltLXhomAa2ra/L3lvguZnZ7Cxnl15JB9xdEgAAqgUCWVQLjWsHa+JlvZXjkBwOeaalH0mfXSR9f4u7SwIAQLVAIItqZ+vBI/JI5k5h1mOqu0sCAEC1QCCLaqNmkL/6tKyr896Yo5ikNHmUuK3S5j/dXQoAAKoVAllUG8EBfpp6y0CZlgXpmR7WTnbpx1JqgtTvBneXBACAaoNAFtXSsl3x8jitT5OaD3B3KQAAqDYIZFHtXNCzqe6LWqnV0YnyCK0GSXVbSy2OBrFpidJ3t0gz/+vukgEAYGsEsqh2Xr2klxrWDFJGdra7i+JkmhPcuULqcoHzdcxaaeWX0pzX3F0yAABsjUAWAAAAtkQgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEsqq1tB5PdXQQAAFCJCGRRLZ3cpr7+77vVmrXpoDxWTraUsMvdpQAAwLYIZFFt7+7VpWltHUnPkkdp2FEKayJ1OFuq21KaNEDKIHMMAMCJ8D+huQCcmCY9pXs3OJ+nJ0nPR0g5HhZsAwBgE2RkAQAAYEsEsgAAALAlAlnA3RKj3V0CAABsiUAWcBe/IKlxD2nyEIJZAABOAIEs4C7+gdLNsyW/QHouAADgBBDIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGypWtyiNj09XePHj1etWrWUnZ2tSZMmubtIAAAA8LaM7IQJE9SqVSv5+PhozZo1+cZt3rxZAwcOVIcOHdS/f3+tW7fOen/q1KkaMmSIJk6cqLp162r+/PluKj0AAAC8NpAdM2aM5syZo5YtWxYaZ7KuN910kzZt2qQHHnhA119/vfX+zp07reDXaNOmjfUaMP5ad0CH0zI9uzJqhkvvnSEd2uLukgAAYCs+DofDIQ9kAtOff/5Z3bp1s17HxMRYmdhDhw7J399fpthNmjTRggULNG/ePGVkZOiaa67RY489prPPPlunnHJKvuW9+uqr1uCSkJBgZXKrSlpamoKDg8u9nNAlr+vUzLn6M3iEcno7A/mCnliYpa+Dn9PhJqdpb/hQeaqKqpPiLDqQo6lbczSiha/OiPC472y5/LNSdMqq+7W27c3aG9S+UuvEjir7OLEb6oM64Tjhs+NN55Prr79e0dHR9m8ju3v3bjVt2tQKYg3T9KBFixbatWuXRo8erVtuuUWrVq2yAtqCQaxxzz33WINLRESERowYUWXlnz59eoWsb8nad6VMqVZYmAYUs7yX1s5UrZBaatmtm7r3rrptdFedFMcsefenS9SpbX2NGNRaHm3r0+rXt6+mb06v0uPSDir7OLEb6oM64Tjhs8P5xIaBrCt4zcuVTA4KCtKHH37oplIBAADAHTz399YCmjdvbqWWs7KycoNYk6U1WVkAAAB4H9sEsuHh4erdu7c+//xz67Vp32ra0bou8gIAAIB38bhA9rbbbrPar5rs6/Dhw9WuXbvccZMnT7YGc9HXCy+8oA8++MCtZQUAAID7eFwbWXMzg+JuaNCxY0f6iEX1lRKrOoejpZzhkq+fu0sDAIDH87hAtrJFRUVZQ2pqqruLAuT3/a0akJUmtQyV+t9I7QAAYLemBZUtMjJSU6ZMUUhIiLuLgiry25r9Gvm/WZq2rPh+6DyCCWKNjGR3lwQAAFvwukAW3mfR9jht2J+kn1bu9dy7fLU/SwrvoqTQllLcVikjxd0lAgDA4xHIwit0blJLS3bE6z/frpJHOvsF6db52tV4hLTuB2nWS+4uEQAAHs/r2sjCOw3t2FCtG9TQjyv2ypNFNxquro0CpUzacAMAcDwEsqjWbjytjZrVDdHYvs21eEecu4sDAAAqEIEsqrV+repZg0EgCwBA9UIbWQAAANiS12Vk6UcWAACgevC6jCz9yAIAAFQPXhfIwnvVDQ3U/G2xuuebFfJovv7S+h+lT86TFr3n7tIAAOCxCGThNc7s0kjPXdRNq/ckyqMNulM6/WGpXltp9bfuLg0AAB7L69rIwrs1qhUsjxdaT+p9heQfJMWsd3dpAADwWGRkAQAAYEsEsgAAALAlAlkAAADYkte1kaUfWQAAgOrB6zKy9CMLAABQPXhdIAsAAAAvbVqQnJysuXPnKjo6WiEhIerZs6e6dOlSOaUDAAAAyhvI7ty5U0888YR+/fVXdevWTY0bN1ZaWpqefvpp+fj46L777tO1115b2sUBAAAAVRPIjhs3Tvfcc4/ee+89+fvnn23Hjh2aPHmy3nzzTd1+++3lKxEAAABQkYHsrFmzih3XqlUrPf/886VdFAAAAOCei722b99e/jUDAAAAVR3IvvTSS+VZJ4CySDssZaZSZwAAnEjTgpYtW6pjx47Wc4fDoY0bN+qtt94qzawAyuPQRumltlLt5tKEZdQlAABlDWTPPPNMvf/++7mvb7nlFtkVd/aCraTGOx8P73F3SQAAsGfTgpdffjnf67ffflt2xZ29YBsdR0nDn5BG5f/8AQCAMgSyderUKc1kACpSYKg0+G6p7RlSTpa05S/qFwCAE73Yy/QjC6CK1WoqdblQ+ny0lJZI9QMAcCKB7IoVK8oyOYCKEBAinT/R+dyRQ50CAFCe7rcAAAAAdyOQBQAAQPUPZE0fsoCdhQb6a0dssu78ermW7ozTgm2x7i4SAACoikD266+/PtH1AB6hf+t6ejmyp35YsVdj3pmvS99doL0J3DULAIBqH8g2atRIn332mZKSkqzXWVlZZGlhOx0ahVmPrh8YsrL5pQEAAK9oI/vss8+qZs2aSk1NVbt27dS4cWPNnTu3ckoHVIKGYUFqWT9UPSJqy8fHZlW8a6G7SwAAgH0D2Ro1asjHx0e//vqrdevaBQsW6KGHHqqc0gGVoEHNIM28/3T9ePtg+fsei2RXRyfqv79v0O64FM+rd/9gqevF0teXSTP/K2VnubtEAAC4nX9ZZwgKCtK+ffus9rLjx49X69atlZycLLuIioqyBpNRBoxVexK0eEecfluzX3+tP6DsHIceGtXZsyrH10+K/Eia20ua+ZIU0U9qe7q7SwUAgL0C2aefflq9e/e2mhScfvrpVhvZI0eOyC4iIyOtISIiwt1FgYe4/cvlso1Bd0rLPjMtfN1dEgAA7BfIDhs2TPv37899vXHjRiugBQAAADyyjezChUVfZNKxY0e988471k/169atq8iyAQAAAOUPZP/73/9q+PDh+uijj6wsbGJiopWZ/ffff/XAAw9owIAB+TK1ACpJaH3pm3HS7sVUMQDAq5W6acHUqVO1ePFiTZ482eqCKzo62urBoEePHho9erTmzZtndcsF2ImPfOzX3vSKKdJnF0mxm6Xm/dxdGgAA7NFGtl+/ftYAVBfvjDtJ+xPT9c2S3Vq5O0G2EFxbCgh1dykAALDfxV5AdXJGp0bW47dLd7u7KAAAoLJviAAAAAB4AgJZwM4cDulIDHf6AgB4JQJZwM4WviO93F7642F3lwQAAHsEsjExMZo9e7b1PCsrSxkZGRVdLgClcXhv/kcAALxImQPZadOmqX///ho3bpz1eu3atbrwwgsro2wAAABAxQWyzz33nJYuXaq6detar3v27KmdO3eWdTEAymveG9KCt53dcQEA4IXK3P2Wr6+v6tevn++9wMBA2UVUVJQ1mFvqArYVWFPaMVs67X4pKEyKXuLuEgEA4PkZ2bCwMB04cEA+PuaOSNKMGTNys7N2EBkZqSlTpigkJMTdRYHNPPPzOo3/bInik/O3Cc/Jcchheg+oShdMkq75RRryoOQbULXrBgDArhnZF198UaNGjdL27ds1dOhQbd68WT/99FPllA7wIJ8u2KmMrBxdNyhJA9rUV0pGlp7/dYM+W7BTl/Vvoecv7l51halRX6oxuORp5r0pHdoonfm0FFKnqkoGAIDnBrKtWrXSP//8o3nz5llZqIEDB1o9FwDeZuLfWzRlyW51a1ZL2w8dkceZ9V8pLVHqcoHUbri7SwMAgPubFpx11lmqXbu2zj77bCszW6dOHes9oDrZEpOkCyfN1cvTNxY53jQnOJKeqatOaanrBrWu8vIBAIAyBLIm65qSkqKcnBzrQinz3Az79u2zHoHqZMXuRK3YnaDf1uwrcvyL0zfo8wW71LQOba0BAPD4QPbZZ59VzZo1tXr1atWoUcN6bobOnTvriiuuqNxSAh5k2rI92rg/Sfee2UHXDGzl7uIAAOC1Sh3IPv7441Y29qabbrIeXUNCQoIeffTRyi0lUMn8fJ29cBQlKztHD3y7UpnZObpreHv9veGA/t14UCGBfrm9dwAAABtc7PX2229XTkkANzI9DizblaCtMUf00dwdatOwhvV+ZrZDexPSNGVJtN6+4iSd3b2J1uxJ1F/rY1Q7xAO7vcpMlX6cIPkHSjnZ7i4NAACeFcgeOnRITz75pFauXKm0tLTc9xctWlTRZQOqTLvwMGswWdf0rBx9PG+HGtcKVnxKhs6fNMeaZkjHhtbja5f00r7ENLUPr+l5eyhpn7R6irtLAQCAZ/ZacN111ykiIkL79++3mhSEh4drxIgRlVM6oIoF+PmqYViQ9dxkZb+9eaASUjLzTRMWHKAOjcJoVgAAgN0C2V27duk///mPgoODdd5552natGlWn7IAAACARweygYGB1mNQUJDi4uLk7++v6OjoyigbAAAAUHFtZDt27GgFsFdeeaVOPvlk6+YIvXv3LutiUIlOzZynkKxt1LGbJKZkKsfhUN0azi99AADAAwLZ7OxsNW3aVPXq1dOdd96pvn37Kj4+3rrLFzzHNelf6HBEHwV3GuXuoniNw6lZOnA4TbWCA3TaSzOUnpWtWfefrvBawVVXiH2rnL0WAADgJcoUyPr5+eXrnWDQoEGVUSZUgP1tRis8pC51WQU6Na6luOQMXf/JYn1xw8lKTHVeHPbn+gO6vH+LqrkobOs/0vofpQBnt2EAAHiDMreRNRd4vfjii4qJicm9TS23qEV11axuiM7s0kgXn9RMQf5+RU7TpWktvTK2p1LSj/XbaqZ/8qd1WrozvmoKmnHE+ZiZXDXrAwDAjm1k77vvPuvxoYcesjJNDofDejTNDoDq4KwujbRu72GN7NZYNYP89d5Vfcu8jKcu6GYFseaGCgAAwEMCWXNbWqA6a98oTJOuOMndxQAAABUdyNpdVFSUNaSmclEMAACAV7WRtbvIyEhNmTJFISEh7i4KUDXWfi+lH21DCwBANeJ1gSzgVU65Xdr8h7Tue3eXBACACkcgC1SQ5Iws3TtlhUxvW75V0ONWiYJrS74B0oDxUsNOzvdWfiM900ia8ZybCwcAgAe0kU1MTNTu3bvVrVu3CioOYE/+vj46cDhdR9Ji9c1Npyg00I3Nz00Ae/tSKSdTqtX02Psx66SsNOnAWveVDQCAClTm/7YjR47U119/LX9/f/Xs2dN676qrrtJTTz1VkeUCbKV/63qaPK6PmtQOVo+IOu4ujlSzobtLAACA5zUtOHDggOrUqaNff/1VF1xwgTZv3qzvv6f9Hbyb6Ut5RNfG7gliA4KPZWIBAPAiZc7IZmY6b785a9YsKzsbEBBQNbfgBFC03ldJtZtLaYnS1OupJQCA1yhzRta0hzUB7M8//6wzzjjDuj0tgSzgRn7+Uvsz87eHBQDAC5Q5I/vf//5Xy5Yts9rHhoaGas+ePbr33nsrp3QAKsb6n6SQusdeZ2dK89+UajWTeoyllgEA3pGRvfDCC62hdevW1utmzZrptddeq4yyAagIAydIhzZLK7869t6eZdJfT0jf3UwdAwCqfyCblZVlNSPIycmxbu9qnpth37591iMAN/MLcj76H310aT9ciuhX8rw/3yN9OFJKjK688gEA4K6mBc8++6yefPJJqz1sjRo1ct+vVasWTQsATxDRRxr3vVSjFF1vmT5m81o7TUqNl2LWS7UjVKky0yS/QMmX+7EAAMqn1P9JHn/8cSsbe9NNN1mPriEhIUGPPvpoOYsBoEK0PV1qfJwblJhmBZ+cL9VsXPWVvuIr6dlG0u8PVv26AQDVTplTIm+//bYVwO7du1e7du3KHQDYRNJeZ7B79Y+SHFLctmPjTBde0UuknOzSLy87yzlP2mFp3yopaX/x08Zvz/8IAEBV9lrwySef6I477rDu7OXn52e9Z5obxMTElKccAKpCeFcpLUFqdaoU1kRq1FWadPKxpgY/TpAyk6WRL0gn31K6ZZreD/563JnhPbJfqt9eumNJpW4GAAAnFMiaW9EuWrRInTp1ogYBu2k5UDrn5WOvb/hbeib82GsTxBrpSSUvx/SCsH+11Pm8Y9OaILY08wIA4K5AtmHDhgSxgLebeoO0b4V0wVvO180HSL7+Uo0G0q6F7i4dAMBLlLmN7MUXX6w333xTcXFxuV1w0f0WULLsHId2x6UoIyunelRVTtbRx6NNEsI7S9f+Kg28063FAgB4lzIHsg8++KAmTJigBg0aKCwsTDVr1rQeARTv3VnbdOp/Z+i5X9dTTQAAuCuQzdv1VnZ2du4jgPxCAvx0/7crtelAkg4dSbfecz3aRlaGtOg9acecss23+ltp/iRp2WfmpFFZpQMAeLkT6pHc9FAwe/bs3Dt+ZWRkVHS5ANv78Jp+qhnkr/X7DstjmRsTDLpLOulqqW6rwuM3T5d+vU+adlPpl2ku+pp6vbR7ofTLPdL+lRVaZAAATjiQnTZtmvr3769x48ZZr9euXasLL7ywrIsBqr2mdUJUKzhAHs3HRzrzSen8iVJAaOHxDkf+x7IY+6kUXLt0826bKa2ZemLrAQB4rTIHss8995yWLl2qunXrWq979uypnTt3VkbZAHiLzy6Uvr3O2RMCAACVFcj6+vqqfv36+d4LDAws62IAeDpzdy/TV2x57VspHTkobf5TyiqmjbDjaDta0542+ZBz2sy08q8bAFCtlbkfWdNDwYEDB6y7eRkzZszIzc4CqEZWfCEteEtqO0yKOcHeFjqOkn77j/Tn41J6orNN7vH8cq+07nvpjEel0+47sfUCALxCmQPZF198UaNGjdL27ds1dOhQbd68WT/99JPsIioqyhpSU1PdXRR4idQMG/bqYe7OFRQmtR4iDb5bmnLViS3HtL3du+xYZjf76IWhWWnOjGtAcOF5zLi8jwAAVFQg26ZNG/3zzz+aN2+eHA6HBg4cqDp16sguIiMjrSEiIsLdRYEXiKgXogenrVbjWkUEbFWpftv8jyWp01KaN1Gq0VCK6F/+dddrK+1fI9VpLiXskmq3kLbPlqaMk66IKv/yAQBeq8xtZNu3b68bbrjBCmLPPvtsWwWxQFV7aUxPjezaWPsPuzm7eNr90mNx0oCbjz/tqfc6u+RKPlgx6478WHosVmoz1Pm67VDpks+lpH0Vs3wAgNcqcyC7a9cunXPOOVYTg+bNm+uhhx7Spk2bKqd0gM35+frI38/ZntytTJt2Xz/nY2mmDazpfB4QUvx0fkHSjOekbf+Wbt353juhLqwBAMinzP9NatSooWuuuUYzZ860hkOHDqlz585lXQwATzbwDunKadKol4qf5tzXpEZdpT1LqrJkAACceBtZ1928fvzxR3300UdatGiRbrnllhNZDCrBx3O361RuCYryMhdhtRvmfB67tehpajSQajU98XWY43T+W1LN8BNfBgDAq5U5kJ0wYYKmTJmi3r17W5nZqVOn0o+sB3nlz006NzhALZrUcndRUIIj6Vny9/VRcECBn9ztpvdVkrkZV68ryz5vzFpp+kOVUSoAgJcocyDbqFEj685ezZo1q5wSodzCQgIU4AntMlGkzQeSdM7EOWpeL0R/33v0AihPZ25YUFR3WS0GOAcAADy5jeyGDRusx4cfftgKZvOaO3duxZcMqKYOHklXRnaO9ibYpJ9U04QgLVGa1K98yzHdeuV9BACgqgLZyy+/PPd5//75+5a84447ylsOoNq6tF8LXdS7mc7p0US2VK+1dMtcKWF3+ZZjuvV6aI/zEQCAqgxkTb+xRT0v6jWAYwa3b6DXLumlHs1q27daSnNr2eMx3XAF1SxdF2AAAFRkG1mfPP988j4v6jWAoiWlZSk6rhrfHrlxN+nU+6Sa+ZsfFdJqsLOLr5B60t9PVlXpAADeGsimpaVp/fr1VvY173PXOAAl69g4TG/O2KJlO+PVtWktbTuYXP2qzD9IGvbo8acLriWd9Yx0eB+BLACg8gPZlJQUjRo1Kvd13udkZIHjG9oxXKufGGE933rwiM6dOMc+1eYfLPkFSC+1d75OiZWCalVM4OtqtmC+GP92v7R/tRRQo/zLBgBUe6UOZHfs2FG5JQHguWo2lG5dIKUlHAtszV29yiu0nnTbQufzD8+W9iyVznpWOsRtrwEAlXRnLwBeqH7bylluvTb5X9dtJcVulpIPSVkZlbNOAEC1QCALwPM06ib9/pAUFGb6fXB3aQAAHopAFoDn6X+jsx1u4m6pwM3EAAAocz+yAAAAgCchIwvAqePZzp4JmvWhRgAAtkAgC8Bp2GPOAQAAm6BpAQAAAGyJQBYAAAC2RCALAAAAWyKQBdzEIYfSs7KpfwAAThCBLOAGtYID5O/rq8EvzlBaJsEsAAAngkAWcIOGYUGacd9QHUxKV0Z2DvsAAIATQCALuElIoB91DwBAORDIAgAAwJYIZAEAAGBLBLIAAACwJQJZAJ4rdptC0mLyv7drgbT+Z8nhcFepAAAegkAWgGdqO0xKPqhOOz7M//7H50rfXCHtXe6ukgEAPASBLOBmpgsuFKF5P2ng7fJxFOieLCfr6CP97wKAtyOQBdwk0M9X7cJr6uzXZ2tfYir7AQCAMiKQBdwk0N9Xf90zRP6+PkpOP5pl9GY+vvkf80o/In1/q/THo9bNfQEAMPypBgAeYcwH0sGNUvszC4+L3SKt+MIdpQIAeDACWQCeoeVA5wAAQCnRtADwAPEpmdq4P8ndxQAAwFbIyAIe4NqPFutIepZu7+6rEe4uDAAANkFGFvAAJog1krnmCwAA7wlk4+Pjdf3116t58+buLgoAAACqeyA7YcIEtWrVSj4+PlqzZk2+cZs3b9bAgQPVoUMH9e/fX+vWrStxWXXr1tUHH3ygjh07VnKpAQAAIG8PZMeMGaM5c+aoZcuWhcaNHz9eN910kzZt2qQHHnjAyrYa+/fv19ChQ/MNd911lxtKDwAAAK+92Ou0004r8v2YmBgtW7ZMf/zxh/V69OjRuv3227Vjxw4rg/vvv/9WcUmByhfk76uUjGzrxggAAMCmvRbs3r1bTZs2lb+/s1im6UGLFi20a9cuK5Atzs0336wNGzZYj/fff7/atm1baJpXX33VGlwSEhI0ffp0VZW0tLQKWV9oqvNWpoeTkopcXlZWllJSUrR66VLFbvXse9FXVJ3Y3Z3dHDqc4aufd+QoMzOLOskj4sBaNcjO0bz581Wwh9kFCxcocV2cvA2fG+qE44TPDucTDw1kXcFrXg7H8W9H+c477xx3mnvuuccaXCIiIjRiRNV1dGQCtopY35K170qZUq2wMA0oYnn+86crNDRUffv0kdoNlyerqDqpLuZOnq+AgATqJK+l+3Rw9iINPOUUaXX++jp5wMlS837yNnxuqBOOEz47nE88tNcC0/NAdHS0lVV0BbEmS2uysgC82OE97i4BAMADeVQgGx4ert69e+vzzz+3Xk+dOtVqUlBSswIA1VvdpI3SN1dKTXq6uygAAA/jlkD2tttus37aN9nX4cOHq127drnjJk+ebA2m+60XXnjB6loLgPfyz06VwrtIl0e5uygAAA/jljaykyZNsoaimP5g58+fX+VlAgAAgL143MVelS0qKsoaUo9e/Q8AAAB78qg2slUhMjJSU6ZMUUhIiLuLAgAAgHLwukAWgI20OlUxdU+Seo9zd0kAAB6IQBaA56rfVss7PSidfLO7SwIA8EAEsgAAALAlAlkAAADYEoEsAPtKTZB2zJWyMtxdEgCAGxDIArCvX++TPh4lzZvo7pIAANyAfmQB2FdGcv5HAIBX8bqMLP3IAgAAVA9eF8gCAACgeiCQBQAAgC0RyAKwpzmvSimx7i4FAMCNCGQB2M95r0t7V0i7F7q7JAAANyKQBWA/bYdJYY3dXQoAgJsRyAIAAMCWCGQBAABgS9wQAQAAALbkdRnZ6n5DhNO1WH6pXMkNL7NrvnRwk7tLAcCO4rZL/74oHdxYuumPHHROH72kskuGUvC6QLa6e0aTlBxxqhTRz91FAapGj0udt6id+z9qHEDZzZ8k/fucNPvV0k2//DPn9H89QW17AALZaiiu921ScG13FwOoGs36SJ3Po7YBwAsRyAIAAMCWCGQBAABgSwSyAAAAsCUCWQAAANgSgSwAe/DxLfq59dpH2jpDWjWlyosFwAss/UT68hIpZoO7SwJvvyECAJuq2VC69CvJkSPVbpZ/XP+bpEObpZVfST3GuquEAKqrFV9IuxdKrU9zd0ng7RnZqKgojR07Vqmpqe4uCoCy6jRK6nxu4fdNd3OtBlOfAOBlvC6Qre539gIAAPAWXhfIAgAAoHogkAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEs4CHCgv31zeYczdl8yN1FAQAUY3tssvW4Ky6FOvIABLKAh3glspdahvlozd5EdxcFAFCM/Ylp1mNSWhZ15AEIZAEPUTs0QDUD3F0KAADsw19eeItaM3CLWgAAAHvzuowst6gFAACoHrwukAUAACiLjKwc6zE7x0HFeRiva1oAAABQFjtik9VB0vLdCbnv+TsypdmvSJmpUp2WUu8rJR8fKraKkZEFYE+D75b6XCt1OsfdJQFQzbkSsVnZzsys0TFznTTnf1JitPTj7VKGs1suVC0CWQD21OV86bz/SbWbubskALxVzUbSyOfdXQqvRiALAAAAWyKQBQAAgC0RyAIAAJRTTFI6degGBLIAAAAnKCUj23o863+zdOCw8/a1qDoEsgAAACcoM9uR29dscnoW9VjFCGQBAABgSwSyAAAAsCUCWQDVS1aG9M8z0vxJkoPbSQKoOPWObFbr2JlFjrvF/0cph6YFVY1AFkD1cnCDNOslafr/SdkZ7i4NgGqkw74f5ePI0SXpj+q7mpdJZz0jBdXWI5nX6ha/HxVweKe7i+h1vC6QjYqK0tixY5WamuruogBFWrMnUQkpGcrOceinlXu1Yf9hagoAPMRq/25a6Oisb2pdrd8ze2rlnkR9nn2msuTn7qJ5JX95mcjISGuIiIhwd1GAQno19NFPO+L11r9b1at5Hd3x1XK1aVBD/9w3lNoCAA+wPzFNwzqF65+NMVqwLU4+Pu4ukXfzukAW8GR9w32lek2UmZ1jDUZmjvMRAOB+7cJrKqR7E/29IcZ6TVN89/K6pgUAAACoHghkAQAAYEsEsgAAALAlAlkAAADYEoEsAAAAbIleCwAP4+/nq+9XRGvHoWR3FwUAUAR/P2efW74+Ug43EHQrAlnAw9wytK3VT+F3y/e4uygAgCKc072JAvx8ZcLZW75YRh25EU0L7GL6w9Kkk9X1yHylO45+/zi0WXpnsPTrA1JOtvTlpaqhVOX4Bbu7tCiHWsEB6tq0FnV4IhJ2S9+NlwJCJR8/6fPRUgaZbcBrzHheemugtGfpiS8jNV768Gzrf65raJ25VRkOv3y/nI3q3sTqU9YlTYEKn36LlLS/vFuBMiCQtYtN06Uekfqk6WPWrfAs+1dJ+1dLm35z3lN+02+6RQ8rs05rd5cWcI/YzVL6EenmOdJNM6Qdc6TkQ+wNwFts/kOKWStFlyOQPbxX2rdCOuuZ3OH5Oo/r75yTSpxtdMYT8ks5KMVuOfF1o8xoWmAnTU/Shj111Emzi51knQhi4eWCakr12zqf+/BdHcAJ8A2Q2g/Pfbk6KFT99X2Js2x1NJPDP4TqrmKc5QEAAGBLBLIAAACwJQJZAAAA2BKBLGATMUlp2pOQ6u5iAABKkJKZrS0xSXI46GC2KhDIAjaQnpWtYS/P1NCXZhDMAoAHm/TPFg1/dZa+XRrt7qJ4Ba/rtSAqKsoaUlPJbME+snMcSkrPsp4nH30EAHieI+mZ1mNCivMRlcvrMrKRkZGaMmWKQkLoIgMAAMDOvC6QBQAAQPVAIAsAAABbIpAFAACALRHIAgAAwJa8rtcC21n3g5SeJKmE/ujSDkubpldlqQAAsKeNv0tJe6WTrpZ8/dxdGpQTgawnSz8iTbmq5GkadpbCO0lRV1dVqQDP1OZ0qdsYqfVp7i4JAE/2zZVSTqYU3lVqMcDdpUE5EcjaXVCYNOwxae137i4J4F51mktjPmAvADgO1y+cFX/nrczsHCKrKkYbWcADdWtWW83qhKhvy7ruLgoAoBROaVNfh46ka9vBZOqrChHIAh7o5Db1NffBM3TfiI7uLgoAoBTev7qv/Hx9lJFV8ZleFI9AFgAAALZEIAsAAABbIpAFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALeIEtMUf0xt+btTsuxd1FAQBbmbXpoPYlpLq7GCiGf3EjAFQfb83YomnL9+jgkXQ9dUE3dxcHAGzj/75brS5Na6lXeh1pr9QuvGbuuBb1Q3X94NaqGeSvkAA/Jbi1pN6JQBYAAKAEN53WVk0XBVuBbIOagbnvB/n76dFzu1B3bkTTAgAAANgSgSwAAABsiUC2KuxbqSFLxkvf3Vz6eVITpPfOkPxDJPnkG3XYEaqTDv0ozX294ssKVEe/PSi91F7aPrt8y8lKlyYPkSYNcH5GAdjX7+a80E7aMafCFnlENTQp8HV18dlRYctEybwukI2KitLYsWOVmlqFVyAe3KjgzHhp98LSz5MSK8Vvl25fJAUea1huvJt9rhY3vNgKkAGUQvRiKTlGOrC2fNWVmSLtWyEd3OD8jAKwr73LpeSD0sH1FbbI+4If1wFHXbXz2Vthy0TJvC6QjYyM1JQpUxQSYjKdHs7HV6rTotDbWfLX4YBwtxQJAAAULdGntpIVTPVUIa8LZAEAAFA9EMgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEsAAAAbIlA1qYysnJynyekZLq1LKhc2dkOHUnPyn19KCld+xPTrMHhcFjvHTicprTMbHYFAJRTQkqGcuTQ4bRM69ybneM8z5ZVUlqmdZ7Oyj72/xoVz78SlolK1rJ+qFZGJ+j8ACk1M1sXvzVP/wRS7dVRo1rBik/J1KjXZ+fu+8vfP3ZjjZcjeyrI31d3fLVcI7s21jvj+rixtABgb2v2JOr+9xbom4AsDX3pXwX4+SgpLUuNagWVaTkNwgI18Z8t1nDD4NZ65NwulVZmb0cga0OX9Guu3tGNpC1SVo5DWUezcqh+WjeooZ8nDNawV2Zar3+4bZBqBQdYz++eskIxSWkK8vezXpvnAIATF5ecoWZ1QlQz219xiRnWezPuG6qW9WuUaTmjujfRI6NG6Z1ZW7V+XxK7pBIRyNqUj4+7S4Cq4ptnZ/vIR76+ztccAgBQ8XwK/IM9esotM3Ouznv+RuWgjSwAAABsiUAWAAAAtkQgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEsAAAAbIlAFgAAALZEIAsAAABbIpAFAACALRHIAgAAwJYIZG0q2yfAenT4BijL4ed808dXOexSoGg+ftK8N6R3T5dWf1s5tXRos/T+mdLfT1WvvbBvlfTeMGn2K1JmqvTlpdLXV0hZ6e4umf1FL5XeO0Oa92bJ0235y3nsrviyqkpmP9Mflg5usP4Xlppr2oBQafN09fr9Yv1f6suSX2DhaV3vFTUuj0yHvzqvm2jtr4uXjFPfxD/KtBkoG/8yTg8PsbHZxVpzOFgDBg3T3p27dXnW4/py/GAlfRDv7qIBnmnkC1JYI2ndD9LmP6XuYyp+HXuWStGLpJRYadhjqjZ2LZD2LJEc2VL3sdKm35zvpyY46xQnbudc53HjFyQNvL346bbOkPYukzb9LvW6nBovyppp0un/J+2YW8bzQmOp5WBp5xxF7z2sqKXRujtypDR5b/5pRzwndTlfantGiYt8IPMmvdbVof6t6ylh5tfqlryA/VWJyMjayBcLdyo+JdN6nuMboC+TeumPPc5vhguyO+rd7fUlh5sLCY8RtWS3Xvhtg/5cd0ArohPcXRz3qxkudblACu/s7pIAOEG741L0xI9rNW/roaInaD6g2IxsjsOhyTO3KjvHUfi8UKO+9XioxUjNDhio7PCuhRdgvrSZaYPCSizjHjXUvmZnWdPG1WhXtg1EmZGRtZFf1+zX3JyGGtCmns7v1VQLtsXqrX+3qlmdEF0+oIWe+3WDu4sID/L0z+t0OC3Lej6kQ0P1aFbb3UUCgHKZtmyPPp63Q9sOJWtg2wZlmjc5U3r+3w26Ifj409YJDdDj53VRRlaOmtcNLfU6/m9UZ62KTtDpncLLVDacODKyNhUeFqwLejWznvv4SNcPbu3uIsGDndOjiRrXDnF3MQDA7Xx9fY47jY+Pj64d1Frjh7Qt1fQuI7s11gMjO6lWsPM6FlQ+AlkAAADYEoEsAAAAbIlAFgAAALZEIAsAAABbIpAFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALAAAAW/KXzc2YMUOff/650tLSVLduXb355pvuLhIAAACqa0Z2woQJatWqlXx8fLRmzZp84zZv3qyBAweqQ4cO6t+/v9atW1fisk4//XR98MEH+uKLL7Rz504lJSVVcukBAADgtYHsmDFjNGfOHLVs2bLQuPHjx+umm27Spk2b9MADD+j666+33t+/f7+GDh2ab7jrrrty5/v555/VpUsXhYWFVem2AAAAwIuaFpx22mlFvh8TE6Nly5bpjz/+sF6PHj1at99+u3bs2GFlcP/9998i53v//fcVHR2tF198sdh1vvrqq9bgkpCQoOnTp6sqNDm4Sj0kJaekaE4p1xmauk8Ds7P11/TpGpadlW9Hbd60WdPTt2rlgRzrdWpqqv7888/c8fPmztPOmj7ydKY5SFXtA7soqk4OpDhyn//9z9+qEeDct3v3Zcvn8H75+xZ9TGdlZeU+N798mOWkZ0vTp++RnZTnODnT4dDsWbPUMzFRO1au1IHoELWJ3qLQ1L1acwLL9M86omFHn8+ePVspIVvyjW9ycHWZP+t2+Ny02LdenSUlJh7WilmzNOTo+zP+/VcZgXXkbnY+l7Tau1EdJcXFx2txCdvQYccOtbaSOge0shTbauc6KcmW7c7/e4cOHSq0fUPS07Ri4UK1jjmguPT12pWQf3x6eroV9jgcDrn+Q66wzguhudOsi8tR8pGcCqu7jIOHVDc11WP3RVo1OE48qo3s7t271bRpU/n7O4tlmh60aNFCu3btsgLZokydOlVPPvmkzjnnHN188816+umn1bBhw0LT3XPPPdbgEhERoREjRqhKrEqUtkg1QkNLv87YrdIaP+f0S/2l7GOj2ndorxFD2ylj5V5p3XKFhITozDOHSLN+t8YPHDRQnRrXkqczH54q2wc2UVSdbD+ULC10fokbdsYw1Q4NsJ7/Fr9c7RuHKcjfT9qyTnXq1NGIEYNy5/OfP91Es9bzbt26KfRgslIysjRiRDfZSbmOkwU+GmK+OMd8rF49e0pdR0izVkmHpGYnsszUeGmx8+mpp54q1W+bf/zK+LJ/1u3wuVm4U9oh1a5dy1mfy5xvnz50qBTWSO5m63PJ3I3STqle3bolb8P0WdI+qXHjRmpcim21dZ2UYMNfm6Udm9SgQQONGNE//8g1wTp5wABpznw1atNZnQfk3/5vfzL/I7Ot2EJH8wO554WjQjYd1C/71mnECNfXtfJZcGCOAg7u12AP3RfTq8Fx4lGBrGEdYHmYb04lMVlbMwAAAMC7eFT3W82bN7eaCLh+EjVBrMnSmqwsAAAA4LGBbHh4uHr37m11p+VqNmCaFBTXrAAAAADeyy2B7G233Wa1UTXZ1+HDh6tdu3a54yZPnmwNpvutF154wepaCwAAAPCINrKTJk2yhqJ07NhR8+fPr/IyAQAAwF487mKvyhYVFWUNpssqAAAA2JdHtZGtCpGRkZoyZYrVZZUnMxe6Zec49P7sbcrIdvabB5TkwOF0LdwWW6pKyslx6OtFuzRjQ0yx0xxJz9KHc7ZrVXRChVd8ZnaOPluwU/O2HpIdZGXn6IuFOzV/a+nqtzyW7IjTx3O3Ky0zT597FexgUrp1btmw/7A+nb9DC0o4bg4dSbfNfoL9RMenWMfi7riU4067JyFVS3fF5863bFe8pizZrT/XHSh2ntSMbH00d7u1jll7C/eCFB2fao0znwnzmftrffHLOlFJaVlauzexwpcLL83I2oX5wDbIzrE+qFcc7YKsZb0ampvn/8mA1vV0fs+m6t2ijgL9fDV+SBsdTs2ypkP10bROsK48uYWC/f1UM7joj2yflnUVGuine6NWas5/zjjuMpfvTtCD01bL39dHW54bVeQ005ZF66mf16ln8zr64bZj/dNWhJkbD+rR79eoYViQFj88XJ5uwbY4PfzdGjULTtPcSl7XA1NXadvBZKu/4It6R1TKOj6cu11v/7s193WzOiGa+2DRx83Hc3fo8Nr9GujsvhioUJNmbNVXi3ZpS8wRvTDa3E6keG/N2KI98Sn67+ge+nZZtC5+a17uuM3Pnq2iDtGfVu3VpBlb1L91Pe0/4tDNQ9rm9gVtPPfrev2aU1uxyRlq27Cmflm1T3ef2aHCtq9do5pat8dHT/+8Tl/fdEqFLRfHEMh6KNf3xreu6CM95ydlSDWC/PJNE14rWBMv6537+qGzzb13UN2YGx48c2H3EqcJDwvStYNa6+5vVpTpCCupl+bcLpyP05fziajERVcKh6vE5qGyb5rnqPy6Kbjskvrrzt12oHKOxiKPyeKmPLtbE43t11wNwgK1aHtcqWbq3KSW9b/U2fl/p3yBbO5kDufnoFOTMF15cktVlAY1gtS6Qagc/LBaabyuaQEAAACqBwJZAAAA2BKBLAAAAGzJ69rI0v0WAABA9eB1GVm7dL8FAACAknldIAsAAIDqgUAWAAAAtkQgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlbogAAAAAW/K6jKxH3hAhO1OK3ynlZDtfJ+6Rb2ayu0sFVA+H90rZ6YXfT4x2fu7MkBLnfC89yTl9VsaxcdmZCkzeq1ClqY6Sjs2fccT6rOZyOKSU2GOvjxw8tty8cnKcyzXrMJL25y9DSdNKCsg8XPS0xTHTmrIYmWnO5TlyFOFzUAHKKjx92mHp8L7SLduqr2KmNfVoxpd1uanx0pEYKSNFSthdeJ9llPLcWJZpC5UhQUo6UMxy90jpR4oe5zpuzH4rcT+kSgm7nMdM7rxpx5ZbcFqzzPTDKpfkQ4WPm4L/ewruMxfXcWN9HrKc+8UqU5KUHOscCjLbZh27Rz97Zp+afVtg//o6sqxj0TwWyfWZLLBc3+x0hSvemreWitgfjmxrutCUPWqQ5fyM+WWlyB38s1LU8GgZlJaYf/+i3LwuI+uR/nxMWvCWNOwxqVkf6dML1Di4njLdXS7A7hp2kj4eJfkFSnVbHXt/8x/Sqq8lH1/nP8aQOtK9m6zPnvYslRp2lg6uz13GgIMb9GtgIzVRnFS3tZSwU/r0Qik1TrrqR6nNEGnlV9L0/5PqtZWS9kn/6yb5B0v3bpQCgo+te97r0l9PSANulnqPkyafZgWWCq7lnDav+W84zw/9b5JGvWQFV0OW3SqtDpHuXS8F1ih5+01g9cZJzqDhjiXS309Lq6fo+pA2ejBom77IGqaHs67PP8/7w6VDm6Qb/3aej0ry4VnOf8o3/CVF9D32/q4F0ocjpEbdpVvmON/76GzpwBrp+j+l5v2LX+ab/ZyBZN2WUuwWaeynUpcLpA2/Sl9fJrU5Xbrq+5LLtfF36atLpNZDpKt/VJm9Pci5D2+ZK4V3Pvb+lr+kz0dLLQdJ1/5aeL4/HpYWvSud+ZQ06M5j75tgdGIvZ1A3YYX0+4PSuu+lC99xjq/Twlk3n1/sPJ4m9nYGtncsk/54RFo7zXmsmmPrRJig6fUekq+/dM86KSjM+f7fT0nzJkpD/09qNdj5WWnSSxo/M//8P93p/LwY4V2kmHWSfKTaEc79b8p2z1opuPaxeRa9J/12v/MYH3y3NGnAsfEmmDPbHLdVdwS30bNB2/RPzDhJJxUuuzlu9q+WrpsuqYZ6xHwnvf6C+tVqowVB261J4lVTyrk4/3yzXrLqeZR8Ncq8ft2hk0OaSudc5Bxfv73SY3dqlyNclapBBzWKeUoTHbOtMiissfN4MPvXHAu1mlTu+r2A12VkPZL5dmo9HnR+a5bkl1aGjAuAot06T3o8XnrkgNS017H3TQDaZqhz3P1bnVkik8FxZUlMEDvieeeXy4MbrLda+R5Qhvnuf+cKyTfAuQwj+eCxz3G3MdK5r0qZKc5/VGkJUk6Br6SudVgZqjjnP/SHdjv/uWdnFHNuOPqYcUR+ORlSeuKxTFdJzDaZbTMZaZMRTXYup2HqNuuxgU9i4XmsaRxFZ9kKsjLQjtzz1rFlHH19dH35tqHgtIXWf9BZZyaIzTef67EUmayyTFvc/K66K27fFaXg/nIx+9XsX3NMmEy+q1yucjbrK105zTmf2XZz3FgZWpPxPChdNNl5rPa97sS2x6zTHJMmq5snu5+vHLnPi6gzM/68idKp9zqD2I6jnF9IEndL5tfDjCRn1rbgPK7luT4HKSYrfMj5PG6rNbpxmvNYrJlVzP88V50fPW5qZDinq3F4mzY6ItQ//S3V90lyfhkseGwOvkffnrNKVzf/Xbp5toIyEo6Nv/JbXdLwe61xtFGl6jRKP5y/Spc1+VW6bZHzC1Le/YtyI5AFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGzJ6/qRjYqKsobU1FR3FwUAAADl4HUZWY+8sxcAAADKzOsCWQAAAFQPBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALAAAAWyKQBQAAgC0RyAIAAMCWCGQBAABgSwSyAAAAsCV/eZmoqChrSE1NdXdRAAAAUA5el5GNjIzUlClTFBIS4u6iAAAAoBy8LpAFAABA9UAgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlr+tH1lM8/N1q7YhN1muX9FJ4Kefx93N+7wg8+gjvFuDnq8/n75SPj4/6tKyrAD8fHUxK10VvzVXT2iHKynHIz9dH2TmOUh0zE//erBkbY6znD4/qnPt+fEqmxn2wUI1qBevlyJ568qe1Wr/vsHXsrtiVoHdnb9PNQ9qqa9NaunfKSmVk56h1gxrWvIeOZKhT4zAt3Rmv5y7qro6Nw/KtMz0rWzd/tlQOOazlb9yfpFfG9lRE3VAt3hGn535dry7BORpRTJmnLN6trxbv0v0jOmpg2waFxj/w7UrtS0zT65f2Vr0agfpr3QEdWLhLV5SifvclpqqJczNKtGhHnJ6fPVevNE1WG6u+MlQ3z/gnflqnzQkO9Yioo4XbYjW5YZoaSpqz5ZAOB+3XqGKW+8OKvfI9uFcXStp04IgeeWe+zmyUpBuPjr/jq+V67orTNG3ZHn2/Yo/13ukdw/XvxhgNaFNfa/YkqmUtXz1Tim1NzczWjZ8usY6hSZJ8jr7/y6p9+nZptEYeZ/7Y5HTVP/p8w/7D+uH3DfqPpISUTD09ZaW1TwsydfHC7xus58M7N9Jtp7crRUmlnbHJeu/71Yo9kmEd2xMv65077s91B/TWv1v0ZESiekjKdjh0/UeLlJiaqQY1g9SsTojW7TusVyJ7qnm90OOu66eVe/XRL3P11AXd1K1ZbVWGvYmp2rblkBKD9umcAuM+nrddffcmKjpwvybPnavzU7brzLBURRSxnNf+3KR5Ww/phdE91LZhzQop2xM/rrU+kx8H5GhjdKI2bYzWGEl7ElIUlpapWnmmnfD1cj19xTDVDg0otJyktCzl/+QXtjchTa/8sdH63HdtWluPntsl3/gXf9+gDUF1dU5g/vl8jh6sl7y7QO+nZOjn+TvUcXusBkhasjNeb8ZuUbvwouvDdV4M8vfVtGXRCg7wU5uGpfjQn4CDR9J137cr9HKlLN27Eci6yXfL9yglI1tbDhwpdSB73aBW6ja4p4Z1Lu0cqM7+c3YnLdwWZz3v16quGoYF6d2r+liB1N3frLTef/uKk6wTvQkUNuxPKnF5Jmgxx5YJOudtjVXtEOc/pF1xKdZglmMCWRNgxSVnWMGsCXyX70rQvxsP5v5THn9aWz3y/Zrc5c7a5By3ZGdcoUDW/IP7fe3+fO+t3XvYCmTnbjlkLTujniusKuyPdQesaeZsPlRkIDt12R4r2Nl+6Ijq1ainmZsOqlZCmlT4f20hO+NS1KTG8YOdNXsOW2XYHpisNnWkPfGp+QJZEwzFZgZq7pZY6/W+IGcga/bTsu1xxQaypqxXN/WXUqVNMUlalBknv4T43EB21uZDVpA+fe1+dWta2wq8X/1zkzVu2a4E63G5X6aeKcW2mi8rJgg0HHWOBbJm//ZtVU9Ds8KlbcXPvzs+NTeQXbwjXlsPHpECZX2pmbosushAdu7WWIUE+Kl7RG3r2CttIGvOm58v2JX7+tCR9NznMzc5j8f1foetQDYzK0eLtsfppTE9dftXy+RwOKdbvSexVIHsgm2xWrY/RAu3x1VaIGs+W+ZYWLo9rlAga46BjqlZmr5mv1r0CFU9BSomKa3IQPbnVXu19WCytf0VFcj+sGKPdWwkt8rShgNJqhXkL6VIMUkZSkpMzRfIzt8aq93xKaodWriezHF6vEC2WZ1g3fXPFuv5lpgjhQJZc0wty0mQChwm1nkqR7phcGtlfp+j75fv0fONzMEnLdweqybNg/XMhd2kNOey8zLns7V7E9Wvdb3cc2mvFnVUGbYdTJaPT4IUVCmL92qk9mykYc0gndOjifWtEWhw9HgwQ3itYCszO7RjuM7u1iS3clo3rKGR3ZrkZvOPx5zQTRB5ouqEBPJFqwLVCy3df72ezetUWPBSUMdGYQoNrJxzTot6oerXsp4qU6C/r/UZ8XWl7myqU+NaalQ72K1lML9qVJa65Vz2qO5Ncr+A1Tn6Jdww57OmdYq+AVKL+qE6u3uTfOdSk7WHvRDIAgAAwJYIZAEAAGBLBLIAAACwJa+72CsqKsoaUlNT3V0UAAAAlIPXZWQjIyM1ZcoUhYTQoBsAAMDOvC6QBQAAQPVAIAsAAABbIpAFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALAAAAWyKQBQAAgC0RyAIAAMCWCGQBAABgSwSyAAAAsCUCWQAAANgSgSwAAABsiUAWAAAAtuQvLxMVFWUNqamp7i4KAAAAysHrMrKRkZGaMmWKQkJC3F0UAAAAlIPXBbIAAACoHghkAQAAYEs+DofDIS8UFBSkhg0bVtn6jhw5opo1a1bZ+uyAOqFOOE743HAu4fzK/xz3OWKD2OTgwYNKT08vdrzXBrJVLSIiQtHR0e4uhkehTqgTjhM+N5xLOL/yP8d9IqpBbELTAgAAANgSgSwAAABsiUC2itxzzz1VtSrboE6oE44TPjecSzi/8j/Hfe6pBrEJbWQBAABgS2RkAQAAYEsEsgAAALAlAtlKtnnzZg0cOFAdOnRQ//79tW7dOnmDVq1aqVOnTurVq5c1fPPNN9b7MTExGjlypNq3b69u3bppzpw5ufOkpKTosssuU7t27az6mjZtmuxswoQJVj34+PhozZo1ue+faB3k5OTojjvuUNu2ba3xb731lqpLnQwdOlRt2rTJPV5ee+01r6iTtLQ0XXjhhdZ2me02x8WOHTu8+jgpqU689TgxzjrrLPXo0cPa7lNPPVUrVqzw6uOkuPrw5mPE5cknn8x3jq32x4jpRxaV5/TTT3d89NFH1vOoqCjHySef7BXV3bJlS8fq1asLvX/ttdc6Hn/8cev5okWLHC1atHBkZmZar5988knH1VdfbT3ftm2bo1GjRo64uDiHXc2cOdOxe/fuQnVxonXwySefOM444wxHVlaWIzY21lru+vXrHdWhToYMGeL46aefipynOtdJamqq45dffnHk5ORYr9944w3HmWee6dXHSUl14q3HiREfH5/7/LvvvnP07t3bq4+T4urDm48RY+nSpY6RI0dax4HrHFvdjxEC2Up04MABR+3atXMPGHNiNgfJ9u3bHd4ayNaoUcMRExOT+7pfv36OGTNmWM+7dOlifchcIiMjc78EVKe6ONE6GDVqlGPKlCm54+6///7ck5PdlCWQ9ZY6MRYvXuxo27at9ZzjpHCdcJw4ffzxx44+ffpwnBRRH958jKSlpVnJMhOQ5j3HVvdzCU0LKtHu3bvVtGlT+fv7W69Nqr9FixbatWuXvMEVV1yh7t2764YbbrBuMRcbG2v9VJH31sDmZ2ZXfZjHli1bFjmuuihPHVT3+rn//vut4+WSSy7Rtm3bct/3pjqZOHGizjvvPI6TIurExZuPk6uuukrNmzfXI488ok8++cTrj5OC9eHtx8hjjz2mK6+8Uq1bt/aq/zkEspXMBK95ecsdgWfNmqWVK1dq2bJlql+/vq6++upS1Ufe8dW1rspTB9W1fj777DOtX79eq1atstq7nXvuuV5XJ88995zVpv7ZZ5+1XnOcFK4Tbz9OPv30UytB8swzz1jBmrcfJ0XVh7ceI/Pnz9fixYt16623FhpX3Y8RAtlKZL4pmnsYZ2Vl5R4E5kNnsrLVnWsbAwICdNddd2n27NlWQGuY7KzLzp07c6c1j66LOgqOqy7KUwfVuX7MZ8V10rz99tutLIrJJHhLnbz88svWRRa//fabQkNDOU6KqBPD248TF5MYmDFjRu5rbz+fuOrDHAveeozMnDlTGzZssLKxJnNqYo8RI0Zo0aJF1f8YcXfbhurOtNfJe7HXgAEDHNXdkSNH8jXEf+WVVxynnnqq9dw0Ks/b6Lx58+a5bYjN+3kbnYeHh1sNzO2uYHvQE60DcxwNGzYst+G9abC/bt06h93rxGz7/v37c8d9++231ra5VPc6MZ+Pk046qdCFjd58nBRVJ958nCQmJjr27NmT+3ratGmOZs2aWdddeONxUlx9ZGRkeO0xUtI5trofIwSylWzDhg1W4+v27dtbjdHXrFnjqO62bt3q6NWrl6N79+6Obt26Oc4///zcC9zMScZcgdyuXTurkfm///6bLwAeO3asdWGHqS8T+NvZrbfeap1c/fz8rIv8XBesnGgdmJOJWWabNm2swVzNXR3qxGyz+WyYY6VHjx7WVbIrVqzwijoxPTiYfIIpe8+ePa2hf//+Xn2cFFcn3nyc7Nq1y7pAx7XtJrhYvny51x4nxdWHNx8jJQWy1f0Y4Ra1AAAAsCXayAIAAMCWCGQBAABgSwSyAAAAsCUCWQAAANgSgSwAAABsiUAWAAAAtkQgCwDHYe6U06lTJ/Xq1St3WLdunW3q7eOPP9aYMWPkCTypLADsz9/dBQAAO/j222/VrVu3Slm2uY21vz+nYwAoKzKyAFAO5p7uL774ogYMGGDd5/yjjz7KHbd582adc8456tevn3r27Km33nor33yvvPKKhg4dqoceekh79uzRsGHD1LVrV5177rnW8Oabb2rv3r1q1KiRUlJScue97LLL9PbbbxcqS0ZGhu6//351797dWt/IkSNzxyUlJVnzmXF9+/a17kFv7N+/X6effrr69OljrXvChAnmjo/WuCeeeEKXX365zjvvPHXp0kVnnHGG4uLicjOr5l7uRS3T+Oyzz6w6OemkkzRkyBCtWbOG4wxAhSMFAAClYH4ODw4Ozn29aNEiBQYGWs/N+wsXLtT69evVv39/jRs3zgpUTRBoAjrTLMEEoieffLI1mODOSE9P17///ms9Hz16tBVQPvLII9q1a5eV/TWBaNOmTTV8+HB9+eWXuuGGG6zA86+//tJ7771XqIzPP/+8tm7dqiVLligoKEgHDx7MHWfKt3LlSrVs2VIPPvigFXxPnjxZderU0U8//aSaNWsqOztbF1xwgaZOnZr787+Zb/HixapXr54uvfRSax4TeJe0zLlz5+rrr7/WrFmzrHLMnj1bV1xxhTUtAFQkAlkAKGfTAhOkGZ07d7aaCJhg8/Dhw1q7dq0V/OXNipq2ta5A9rrrrssdN2PGDE2cONF63qJFCys763LnnXdq/PjxViBrAkUTIJvAs6Cff/7ZyvKa4NFo2LBh7rjBgwdbAadxyimn6I033rCe5+Tk6D//+Y/mzJljZWJjYmKsNsCuQPbss8+2gljXfKtXrz7uMn/44QcraDUZWRcTVJuMMQBUJAJZACinvJlaPz8/q82rCQobNGigFStWFDtfwWDUZHGLYrK8Zh0zZ860MrH//PNPhZTRePXVVxUbG2tlV80099xzj9LS0o47X0njzLabIP2pp54qczkBoCxoIwsAlaBjx44KDQ3Vp59+mvveli1bctuYFmTaypp2p8bu3bsLBasmK3vllVda7Vg7dOhQ5DLOP/98/e9//7OaLBh5mxYUJz4+Xo0bN7aC0gMHDigqKkrlZdrUmu022+HK+prmDgBQ0QhkAaAUzE/tebvfMu0+S2KaGJi2p1OmTFGPHj2sANQ0DUhNTS1y+tdff11//vmndZGWyYoOGjRItWvXzrf+I0eO6Pbbby92naaJQNu2bdW7d2+rjFdfffVxt8tc3DVv3jxrepNFNe1xy+u0007Tc889Z7W3NdtjmmR888035V4uABTk43BdngoAcBsT4AYEBFgB8L59+6yeDv7++28rs+u6uMxkZDds2CBfX3IQAGDQRhYAPIDpquuqq66y2pdmZmbq8ccfzw1iTSb3jz/+0Pvvv08QCwB5kJEFAACALfH7FAAAAGyJQBYAAAC2RCALAAAAWyKQBQAAgC0RyAIAAMCWCGQBAAAgO/p/pnQYcyy+yDEAAAAASUVORK5CYII=", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for idx, (spct1, spct2) in enumerate(zip(summed_spectra, single_ch_spectra)):\n", + " plt.figure(figsize=(10,10), dpi=80)\n", + " plt.hist(spct2, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(spct2), log=True, histtype='step', label='SiPM-Ch.1-4')\n", + " plt.hist(spct1, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(spct1), log=True, histtype='step', label='SiPM-Ch.5-8')\n", + " plt.legend()\n", + " plt.xlabel(r'Energy channel')\n", + " plt.ylabel(r'Count rate ($s^{-1}$)')\n", + " plt.title('Current comparator background spectra, SIPHRA-Channel '+legend[idx])\n", + " plt.xticks(np.arange(0,4500,500))\n", + " plt.grid()\n", + " plt.show()" + ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Charge Comparator background spectra\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Parameter
Value
Inactive channels
Active channel
ccmis_detector_voffset
127
cmis_detector_ioffset
7
cc_threshold
1
" + ], + "id": "526643d5d320fa61" + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/260205.ipynb b/notebooks/260205.ipynb new file mode 100644 index 0000000..20836a1 --- /dev/null +++ b/notebooks/260205.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:26:51.313673500Z", + "start_time": "2026-02-06T13:26:51.261224100Z" + } + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b55b402-ac25-4996-9c5b-763602446cbb", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:26:55.213845600Z", + "start_time": "2026-02-06T13:26:51.321486100Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 Detector ID Trigger Time_sub Time_sec Time_gps \\\n0 0 5.0 401139.0 21.0 2310347.0 9687.0 42069.0 \n1 1 5.0 401140.0 20.0 2310347.0 9687.0 42069.0 \n2 2 5.0 401141.0 21.0 3799888.0 9687.0 42069.0 \n3 3 5.0 401142.0 20.0 3799888.0 9687.0 42069.0 \n4 4 5.0 401143.0 21.0 3949203.0 9687.0 42069.0 \n... ... ... ... ... ... ... ... \n99831 99831 5.0 501130.0 21.0 8088842.0 9856.0 42069.0 \n99832 99832 5.0 501131.0 21.0 8230393.0 9856.0 42069.0 \n99833 99833 5.0 501132.0 20.0 8230393.0 9856.0 42069.0 \n99834 99834 5.0 501133.0 21.0 8416551.0 9856.0 42069.0 \n99835 99835 5.0 501134.0 21.0 8457518.0 9856.0 42069.0 \n\n Temp Ch1 Ch2 ... Ch9 Ch10 Ch11 Ch12 Ch13 \\\n0 149.432817 150.0 230.0 ... 129.0 140.0 123.0 117.0 120.0 \n1 149.432817 149.0 266.0 ... 129.0 138.0 122.0 116.0 120.0 \n2 149.432817 150.0 211.0 ... 127.0 140.0 123.0 116.0 119.0 \n3 149.432817 150.0 626.0 ... 130.0 141.0 123.0 118.0 121.0 \n4 149.432817 150.0 225.0 ... 127.0 140.0 122.0 117.0 119.0 \n... ... ... ... ... ... ... ... ... ... \n99831 149.432817 150.0 474.0 ... 129.0 139.0 123.0 117.0 120.0 \n99832 149.432817 152.0 559.0 ... 129.0 140.0 123.0 118.0 120.0 \n99833 149.432817 150.0 862.0 ... 129.0 141.0 124.0 118.0 121.0 \n99834 149.432817 151.0 492.0 ... 129.0 141.0 124.0 118.0 120.0 \n99835 149.432817 149.0 300.0 ... 129.0 139.0 124.0 116.0 120.0 \n\n Ch14 Ch15 Ch16 Argmax Summed \n0 118.0 108.0 135.0 2.0 2086.0 \n1 118.0 106.0 135.0 2.0 2113.0 \n2 118.0 108.0 135.0 2.0 2062.0 \n3 119.0 108.0 135.0 2.0 2487.0 \n4 119.0 107.0 135.0 2.0 2072.0 \n... ... ... ... ... ... \n99831 119.0 109.0 136.0 2.0 2331.0 \n99832 119.0 107.0 135.0 2.0 2423.0 \n99833 119.0 109.0 136.0 2.0 2727.0 \n99834 119.0 108.0 136.0 2.0 2354.0 \n99835 119.0 108.0 135.0 2.0 2157.0 \n\n[99836 rows x 26 columns]", + "text/html": "
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Unnamed: 0DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
005.0401139.021.02310347.09687.042069.0149.432817150.0230.0...129.0140.0123.0117.0120.0118.0108.0135.02.02086.0
115.0401140.020.02310347.09687.042069.0149.432817149.0266.0...129.0138.0122.0116.0120.0118.0106.0135.02.02113.0
225.0401141.021.03799888.09687.042069.0149.432817150.0211.0...127.0140.0123.0116.0119.0118.0108.0135.02.02062.0
335.0401142.020.03799888.09687.042069.0149.432817150.0626.0...130.0141.0123.0118.0121.0119.0108.0135.02.02487.0
445.0401143.021.03949203.09687.042069.0149.432817150.0225.0...127.0140.0122.0117.0119.0119.0107.0135.02.02072.0
..................................................................
99831998315.0501130.021.08088842.09856.042069.0149.432817150.0474.0...129.0139.0123.0117.0120.0119.0109.0136.02.02331.0
99832998325.0501131.021.08230393.09856.042069.0149.432817152.0559.0...129.0140.0123.0118.0120.0119.0107.0135.02.02423.0
99833998335.0501132.020.08230393.09856.042069.0149.432817150.0862.0...129.0141.0124.0118.0121.0119.0109.0136.02.02727.0
99834998345.0501133.021.08416551.09856.042069.0149.432817151.0492.0...129.0141.0124.0118.0120.0119.0108.0136.02.02354.0
99835998355.0501134.021.08457518.09856.042069.0149.432817149.0300.0...129.0139.0124.0116.0120.0119.0108.0135.02.02157.0
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99836 rows × 26 columns

\n
" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "dfs = []\n", + "dfs.append(pd.read_csv('../data/260205/5_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260205/6_SiPM_ChannelsTest_Ch5-8_Ch6_QT_Thr20_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260205/7_SiPM_ChannelsTest_Ch5-8_Ch10_QT_Thr20_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260205/8_SiPM_ChannelsTest_Ch5-8_Ch14_QT_Thr20_Background.csv'))\n", + "\n", + "dfs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cb4c1aad-40db-456b-a316-8ce713563841", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:26:57.518543800Z", + "start_time": "2026-02-06T13:26:55.204462Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N_BINS = 512\n", + "BITS_12 = 2**12\n", + "\n", + "summed_spectra = [df[ch].tolist() for df, ch in zip(dfs, ['Ch2', 'Ch6', 'Ch10', 'Ch14'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a8cf1914-e98b-44cc-b77e-46ec5b15245f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:26:58.797329900Z", + "start_time": "2026-02-06T13:26:57.507242Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [162.847, 180.959, 169.660, 136.229]\n", + "plt.hist(dfs[0]['Ch16'], N_BINS, range=(0,BITS_12), log=True, histtype='step')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "436876e4-6c49-4cc6-9552-6551a7059e27", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:27:01.546487700Z", + "start_time": "2026-02-06T13:26:58.802252300Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [162.847, 180.959, 169.660, 136.229]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Charge comparator background spectra, SiPM-Channels 5-8')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e68ab9ee-bdeb-4c34-b3ef-e032a4b3a6ec", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:27:03.307130800Z", + "start_time": "2026-02-06T13:27:01.547464200Z" + } + }, + "outputs": [], + "source": [ + "dfs_ch1_4 = []\n", + "dfs_ch1_4.append(pd.read_csv('../data/260205/9_SiPM_ChannelsTest_Ch1-4_Ch14_QT_Thr20_Background_retry.csv'))\n", + "dfs_ch1_4.append(pd.read_csv('../data/260205/10_SiPM_ChannelsTest_Ch1-4_Ch10_QT_Thr20_Background_retry.csv'))\n", + "dfs_ch1_4.append(pd.read_csv('../data/260205/11_SiPM_ChannelsTest_Ch1-4_Ch6_QT_Thr20_Background_retry.csv'))\n", + "dfs_ch1_4.append(pd.read_csv('../data/260205/12_SiPM_ChannelsTest_Ch1-4_Ch2_QT_Thr20_Background_retry.csv'))\n", + "\n", + "#dfs_ch1_4 = dfs_ch1_4[::-1]\n", + "single_ch_spectra = [df[ch].tolist() for df, ch in zip(dfs_ch1_4, ['Ch14', 'Ch10', 'Ch6', 'Ch2'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "97eb2049-7aab-4804-a96b-555d5d4c84f9", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:27:05.647528100Z", + "start_time": "2026-02-06T13:27:03.309083800Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times_ch1_4 = [47.626, 86.144, 116.448, 72.604]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.14', 'Ch.10', 'Ch.6', 'Ch.2']\n", + "for idx,s in enumerate(single_ch_spectra):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Charge comparator background spectra, SiPM-Channels 1-4')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "aee0452b-0a8b-4f2f-83e7-94bed99372dd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T13:27:11.969887700Z", + "start_time": "2026-02-06T13:27:05.634146Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for idx, (spct1, spct2) in enumerate(zip(summed_spectra, single_ch_spectra)):\n", + " plt.figure(figsize=(10,10), dpi=80)\n", + " plt.hist(spct2, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(spct2), log=True, histtype='step', label='SiPM-Ch.1-4')\n", + " plt.hist(spct1, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(spct1), log=True, histtype='step', label='SiPM-Ch.5-8')\n", + " plt.legend()\n", + " plt.xlabel(r'Energy channel')\n", + " plt.ylabel(r'Count rate ($s^{-1}$)')\n", + " plt.title('Current comparator background spectra, SIPHRA-Channel '+legend[idx])\n", + " plt.xticks(np.arange(0,4500,500))\n", + " plt.grid()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2026-02-06T13:27:11.972817300Z", + "start_time": "2026-02-06T13:27:11.964869500Z" + } + }, + "id": "5d4f19c5bbfd41eb", + "execution_count": 8 + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/260206_CMIS_voffset.ipynb b/notebooks/260206_CMIS_voffset.ipynb new file mode 100644 index 0000000..0ccf543 --- /dev/null +++ b/notebooks/260206_CMIS_voffset.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T10:44:04.423897Z", + "start_time": "2026-02-06T10:44:04.420846Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f22322a36b757cda", + "metadata": {}, + "source": [ + "# CMIS input voltage offset comparison\n", + "\n", + "We acquired spectra from SIPHRA channel 2 using QC, feeding from SiPM channels 5-8 and compared the output for different values of the `cmis_detector_voffset` parameter (Range: 0 - 255). This parameter controls the voltage accross Vin (see schematic below), which is used to downscalethe input current. The CMIS input offset voltage modifies the SiPM bias voltage, allowing for gain control. The effect of modifying this parameter is clearly seen in the spectra below. Notice that higher values of `cmis_detector_voffset` correspond to higher SiPM gain (i.e. lower Vin voltage), which is clear from the decrease in dynamic range and increase in events triggered from noise (lower energy peak).\n", + "\n", + "**We might want to keep this parameter at 0 to maximize dynamic range.**\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "75bebada911337a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T15:07:25.679855Z", + "start_time": "2026-02-06T15:07:25.151406Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "dfs = []\n", + "dfs.append(pd.read_csv('../data/260206/1_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset0_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260206/4_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset50_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260206/2_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset127_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260206/3_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset255_Background.csv'))\n", + "\n", + "# dfs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "cb4c1aad-40db-456b-a316-8ce713563841", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T15:07:26.798447Z", + "start_time": "2026-02-06T15:07:26.789831Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "BITS_12 = 2**12\n", + "N_BINS = 512\n", + "\n", + "summed_spectra = [df['Ch2'].tolist() for df in dfs]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "436876e4-6c49-4cc6-9552-6551a7059e27", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T15:07:28.977212Z", + "start_time": "2026-02-06T15:07:28.227680Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "image/png": 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K8Q+n+GlXtE8cQ2THRNakbFZWZIxE6bCicj0iwBVZGfGzoRgQsn79+uJ/LCvLhoqskqhDKgqwi7aL4F0Moio5cEoMZhJfAMT9mzdvlkF0dYhsljiGaJv4B1QEQhX9Q15EZH/EACwRZIvAWQzOKvpCUEQMZhPZMnER9U/F+RPZUNHFQmReH3zwQZm5Fpkr8TqIn2CLSj+JwWZi8It4HPEYIhAWg+CKfsIVwaF4TPGzemVfLKp6TauSmJgoXwMRMIpBO6Itok3iy5UYBNXQRLksEciI911R9wFxXkWQXnQRgaT4slDez+2CeD+Jz4B4TURQLL60iJ/0RRkn8R4Vz9EW4jURQeeFCxfkbbEUX0bFz/7itRRfrsSlsi4w4nMssubiC4f4vIvBbOK1FzWhqxocZQuRQRefz969e9u0vQgERYAvBnKJz7n4MiXaJboKiYGh4kuDLSr7fIjzJM5b0Rc98eVWvB7i/Sk+Cz/88IMcwPjwww9X+LdBnBvxWolss/h8iQGrotyZ2F+8X4nqlM29aYlIOX78uCx9ExAQUFxe6Y033rjszIgSXWJQRJ8+feRAGLG9GCC0fPnyy8p3lRyMJcoX3XbbbbIUT0REhCzxVCQpKUkOJBPHE2WqxH1i0I4YdFNEDCIZN26c3F8Mqhk9enTxwJCyJXRWr14tt1uxYoW8ffbsWdlGUeKqe/fuyjfffCOfnxiwUllZJlF+a+TIkRWW3xLll4YOHSofq3fv3sqvv/5abvmtksoOeik6hhicIwbdiOde2WAvMVCtW7duxedevGZFz6No8JMYgNSmTRv5fMXgvKIyZUVljsSgKXGeXVxc5Gtxxx13FB9DWLx4sSz5VHT/m2++WXzfxx9/rDRr1kwOEipZfqtoUFB1XlOxX9ExyhtQ8/TTT8vBUWLAl7iI8lfi+VenbFZJtSm/JYgyT2FhYfJ5iRJQ5RHvwy5duiiVEe/LwYMHy0Fs4r3cv39/5csvvyx+/PLOZ8mBhUXtLTtYsexFfC4qsnTpUmXMmDFyoJJ4n4iSeGIwWmJios3lt0qq6v6qBnsVEYOmRNmwwMBA+bnr1KmTHHxV9B4uKr9VUsnPf2Wfj6I2Fg1qEwPeRFk4Pz8/+VjiPS8GqRkMhkrbKAYc3nLLLUpISIgcENi3b99ySxIS1ZZK/K9uQ2Mi5yB+XhQ/CYushvh5MDAwEE2JyJiJrKT4uVJkgpoKkcUSmTnx02dlpdAaA1GGSwywExlW0WYiIiqt6fzrRNTARBAkasaKn4O3bt3q8Odf9FsUXQPEz39iUIv4+VX8LNyUglhHI74giZ/iGcQSEZWPGVkikor6zYo+d2KA1o033ij78JWdftPROVJGloiIKsdAloiIiIgcEn8zJCIiIiKHxECWiIiIiByS03YQE4XHRT/AhiKKZ4vHJJ4Tvk/42eHfEv595b85DY//DjvmORH1uCubgMRpA1kRxMbFxTXY44lC2mXn0nZ2PCc8J3yf8HPDvyX8+8p/c+xnjQPEJhEREZXez64FREREROSQGMgSERERkUNy2q4FRERE1HhYLBbU92SjZrO5Xo/viMx2PicqlapWE+8wkCUiIiK7SU9PlwN66jugEmNjTp48Wa+P4WiCG8k5cXd3R4sWLWoU0DaJQHbjxo1yXviieck/+eQTezeJiIiIbAhik5KSEB4eLoMZkZ2rL1lZWfDx8eFr0sjOicjCX7hwQb4PmjVr5viB7KOPPoqVK1fKaTIPHTqErl27Ft936tQpOa99SkoK/Pz88M0336Bz585yusmiucjHjx+P7OxseHt72/FZEBERUVVEJlYEsV5eXvV+skS2T6PR8EVphOckNDQU586dk8vqfplpdIO9Jk2ahH/++UemmMuaMWMG7r//fpkGf+aZZzB9+vRS9//2228ysGUQS0RE1Pj7xIruBCITS87NxcVFZmZr0kdapdR3z+oaatmypQxMizKyIuXcvn17mY3VarXyyTZv3hw7duyQ23755ZeyLuyrr75a7vHmzp0rL0UyMjKwYsWKBns+otsDP6w8J3yf8LPDvyX8+8p/c0r30WzVqlWtBvvYSsQN9dl1wREpjeSciC810dHRMkNflkhaVlb3v9F1LajI+fPnERYWJoNYQZz4qKgoxMbGYs+ePZg9ezauvfZaPPDAA3j99dcvm7XrySeflJeSBXYbsgiwIxQdbmg8JzwnfJ/wc8O/Jc7791VkY8UvrKKPZkP8vJ2ZmQlfX996fxxHktlIzol4L+h0OowcObLa7wWHCWSFst8aipLJEydOlBciIiIich6Nro9sRSIjI2Vq2WQyFQexIksrsrJERERERS5evFg8CLy2hg4dKrs6VuWjjz6S3SBrY9OmTfjrr7/QGOzYsQPdunVDr169ZIa/7O3qEoO5vvjiC+cNZENCQuTJE2W2BNG/VfSNFRciIiKiIqIroijN2ZDsHcia67gO77fffou77roL+/btk91Uyt6uLqcJZB9++GHZf1VkX0VfibZt2xbft2DBAnkRg77efvttLFy40K5tJSIiovqxfft2DBkyBD169ED37t3x66+/yuTVyy+/jEGDBslfZEVy6+OPP0b//v3Rpk0bGQgWBU1BQUHyul6vx80334wBAwbIY40ePbrSxz169Kjctnfv3rj99tvlYO0iCQkJmDJlinw80SbRFuG1116TWWBRealnz57Yv38/jEYjnnvuObmtWHfLLbfIgeZFfVPvvfdemeEUbbrnnnvkPp9//jn+97//ye3FMYVFixbJ7cTjibFAouaqIEqQjh07VgaXffv2xc6dO8t9PiKWKjm4XQT44rkJIvC+8cYb5fHF4PqiQFPEWD/++KM8t6ItZW+npaVh5syZ6Nixo2x/nz59is+TyNYOHjxYrhPnccuWLXK9GMMkzq3Y//rrr0edUZzM0qVLlcmTJysBAQEN+rirV69u0MdzBDwnPCd8n/Bzw78lzvv31WQyKUePHpXLslJTU5XQ0FBl69at8rbZbJbrWrRooTz11FNy3c6dOxWdTqfMnz9f3v7xxx+VK664Ql6Pjo5WAgMD5fWffvpJGTVqlJKRkVF87Mr07t1b+eabb+T17du3K2q1Wlm1apW8PXr0aGXz5s3yutFoVMaMGSOPL4i2HTp0qPg4c+bMUV5//fXi26+99pry6KOPyuvTpk1TZs6cKZ+XkJSUJJevvPKKMmvWrOJ9xPHEeYiLi5O333jjDWXcuHHy+tdff614enoqJ0+erPT5fPfdd8q1115bfPuuu+5S5s2bJ6/feOONynPPPSevJyYmKhEREcq///4rb0+dOlX573//W7xfydt79+5VOnbsWNx+cW7F9TNnzsjXIDMzU64/deqUEhYWphgMBmXjxo1Knz59qv1eCA8Pr/T5OdRgr7owefJkeRFZXyIiImqc2VhRF15kXgVRnisgIEBeF9lVQWQVRbZVZEgFkQE8e/bsZccSGcPjx49j1qxZGDVqFMaNG1fpTFeHDx/GnXfeKW8PHDhQZiuF3NxcbNiwAYmJicXb5+TkyGOX55dffpHHW758ubxtMBhk1lgQfW5FxaWismNlKy2VzJ5ed911ctII4aGHHsIbb7xRPNh98ODBaNeuXaXn8qabbpKTTYlssqenJ1atWlVcjlRksOfNm1fchVNsu379eplFrkzr1q1lxllkkkVfZJEpFs9l9erVOH36NK666qpS24sxTfXF6QJZIiIiclxFNdmLyjSVvF00ILxs0CV+0hYB3NatW+WESuJnfDGlfXkqqqsqap2K+3bt2iUL+FdFBJuffvophg8fXq3nV/YYJdtTtm1eNsyIJs6P6PIgumGI5yy6GgQGBlZ4TFvqyoqSXUeOHMHmzZtlsP3888/LLgSivaK7g+geUZYol1ofGl0fWSIiInJuIhN77NgxbNu2rTiIFP0ya0KMuRHBmcjEvv/++8VVj8ojatqKvqLfffedvC36nR46dEheF7OGij67or9oEdEvtqhYv9hX9H0tIvqBisxnXl6evC2WIvgruu+9996Tz0somgig7DFGjBiBP/74Q2ZTBdGHVqyr7iQG99xzj+xT+/XXX+Puu+8uVZGhqF+saMPPP/9sU+AtthUZatHf+M0335R9l8WXBXFbZGVFVrtIUd/dss+trjCQJSIiokZFZA5FUPX000/LQU6iapGYvr4mRCAqAmNxEd0RRLcBccyKiGziJ598IrcVQZ4YsFREBLgiwBbdDcRF1LBPTU2V94mf70WQWDTYSwz0EtfF/uLxRDcFsV748MMPZWArgmaxzX/+8x+5Xgy82r17d/Fgry5duuCtt96SAaI4xt9//y0HvVdX/8KuAmL2rJKD3d555x0cPHhQHlt0EXjhhReq7FYgiC8CopuG2K9ooNg111wjuzmIzK8YyCa6dHTq1EkOEBPEth06dJDb1uVgr0Y7RW19K6qM0FAcYZaVhsZzwnPC9wk/N/xb4rx/X4tm9hKViDizl31kNqKZvSp6L1QVrzEjS0REREQOyekGey1btkxexEhHIiIicj6i32nRz/kliUFLRVURHM31119/2YAq0UWjoSeGaGhOF8iy/BYREZFzEwO/KivD5YhWrlwJZ8SuBURERETkkJwuI0tERERki3yjGUaztURWXXLRqOHuUnpQE9UMA1kiIiKicoLYIe9uRHJ2QZ2fm2BvN/z9zDAGs3WAgWwTk/D6Gyg4fRph774Ll9AQezeHiIjIIYlMrAhitz8/HF5udRcu5RSYcMVbG+TxbcnKnjp1ClOnTkVKSgr8/PzkxAZi+l6yYiDbxGSuXAlLdjYKTp5kIEtERFRLIoj1dq96Str6MmPGDNx///2YNm0ali9fjunTp2P79u12a09j43SDvUTprSlTprD8FhERETVqSUlJ2Lt3L+644w55W8wkJmbnOnfunL2b1mionbH81tKlS6HT6ezdFCIiIqJKp4INCwuDVmv9AV2lUiEqKuqyerHOzOkCWSIiIiJHIYLXkhRFsVtbGiMGskRERESNUGRkJOLi4mAymYqDWJGlFVlZsuJgryYq9Ysv4NamNVzCwuzdFCIiIoclqgzY63ghISHo1asXFi9eLAd7rVixAi1btpQXsmIg2wQ1e2020hZ+hdwd/8Lvphvt3RwiIiKHIyYtEPVeRamsuiaOK45viwULFsgg9s0334SPjw++/fbbOm+PI2Mg24TknzwJxWSCrkdPaMOa27s5REREDkvUeBWTFth7Zq8OHTqw3FYlGMg2ERaDAecmToJ7585waRZq7+YQERE5PBFscirZxo2DvZoKiwWK0YiIzz+DxtfX3q0hIiIiqndaZ5wQQVz0er29m0JEREREteB0GVlOiEBERETUNDhdIEtERERETYPTdS0gIiIisokxHzAb6v5kaVwBF3e+CHWAgWwTZjh3TpbjUhXO0UxERETVCGI/7g7kJNb9KfMKBR47yGC2DjDCaaI8+vVD6pcLoQ0KQsBdd9q7OURERI5FZGJFEPvEUcDNu+6OW5ANfNjZenwbsrJiFi93d3d5EZ5//nncfPPNSEpKwl133YUzZ87Azc0Nn3/+OQYPHgxnw0C2iQp+6CEYY2Jgycuzd1OIiIgclwhi3X3s2oTly5eja9eupdY999xzGDhwIFavXo1du3Zh0qRJMqjVOtmvsM71bImIiIiagKVLlyI6Olpe79evH0JDQ/HPP/9g6NChcCYMZImIiIgasdtvvx0WiwUDBgzAW2+9BbVaLW8HBweX6oIQGxsLZ8PyW0RERESN1JYtW3DgwAHs3bsXgYGBmDp1qlyvUqlKbacoCpyR02VkObMXEREROYqoqCi5dHFxweOPP4727dvLgFZITk4uzsrGxMQUb+tMnC4j60wze6nc3JH2v/8ha/UaezeFiIjIMYkqA/lZdXcRx7NRbm4uMjIyim8vWbIEvXr1Ko5n5s+fL6+LwV4JCQmsWkBNS8hTs2DOykLezp3wGTvG3s0hIiJyHGLSAlHvVZTKqmviuOL4VUhMTMTEiRNhNptl14HWrVvjf//7n7zvnXfewZ133ol27drB1dUVixYtcrqKBYLzPeMmquDU6cvWaXx84BoZAUsuS3ARERFVi6jxKiYtsOPMXiJw3bdvX7n3iSoFf/31F5wdA9kmwJiYiHO33CInQVB7etq7OURERE2DCDY5lWyjxkC2CVAKCqBSq9FikfXnBiIiIiJn4HSDvYiIiIioaWAgS0REREQOiYEsERERETkk9pElIiIiKkeBuQBGs7HOz42LxgVuGjee8zrAQJaIiIionCB27IqxSNGn1Pm5CdIFYfXE1VUGs48++ihWrlwpZ+06dOgQunbtKtffc8892Lp1q5zcycfHB/PmzUPPnj3lfZMmTcLp05dKch48eBC//PILrr/++ib5GjOQdQKKxVpIuey8zERERFQ+kYkVQezaSWvh5eJVZ6cpx5iDUctHyeNXFciKoPSZZ565bMauG264AV988YWcAOG3337DlClTcPLkSXnf8uXLi7fbvXs3xo4dizFjmu6kSAxkmzhtSChS/+9LWV829Omn7d0cIiIihyKCWC/Xugtkq+Oqq64qd33J7OrAgQNlxtZisUCtLj306auvvsIdd9wBN7em243B6QLZZcuWyYter4cz8L/jdlhyspF/9Ki9m0JERER17OOPP8a4ceMuC2Lz8/OxZMkSbNmypUmfc6cLZCdPniwvERERcAaiO4Ha0z7fJImIiKj+LF68GEuXLsXff/992X0rVqxAu3bt0K1btyb9EjhdIEtERETk6H788UfMnj0b69evR0hIyGX3L1y4ENOnT0dTx0CWiIiIqJLBWY3teCIL++KLL2LdunWIioq67P7o6Gjs3LlTVito6hjIEhEREZVT61WUyRIVBuqaOK44flUefvhh/Prrr0hISMDIkSPh5eUlS2vdfvvtaNasGSZMmFC8rcjMBgYGFg/ymjhxoizN1dQxkCUiIiIqQ5TGErVe7Tkhwvz58+WlLKOx8ja9/vrrcBYMZImIiIjKIYJNzsDVuJWu1UBERERE5CAYyBIRERGRQ2IgS0REREQOiYEsERERETkkDvYiIiIiKoeloABKFRUCakLl4gK1W9VVC6hqDGSdgMbfD9kbNiJ+9mw0f+UVezeHiIjIIYLY0yNHwpycUufH1gQHoe26dQxm6wADWSfgc911MKelIfOPP+zdFCIiIocgMrEiiG27aSPUXl51dlxLTg5ODx1mzfRWkZXNz8/HLbfcgqNHj8LDw0NOgvD555+jZcuWGDp0KGJjY4snPZg6dSqeeOIJeX3atGly1q+goCB5e9SoUXjvvffQFDGQdQIqlQra0FB7N4OIiMjhiCBWU4eBbHXdf//9uOaaa+S/5Z988om8/ddff8n75s2bh+uuu67c/Z577jnMnDkTTR0HexERERE1Qu7u7hg3bpwMYoWBAwfi7Nmz9m5Wo+J0geyyZcswZcoU6PV6ezeFiIiIyGYiAzt+/Pji208//TS6deuGm2+++bIAd+7cuejevbvM2O7fv7/JnmWn61owefJkeYmIiLB3U4iIiIhs8uabb+LUqVOyj6ywaNEiREZGQlEUzJ8/Xwasoi+tMGfOHDRv3hxqtRo///yz7Jog9vWyYxeJ+uJ0GVkiIiIiR/L+++/jp59+wp9//ikHfQkiiBVEtwPRF1ZkZFNTU+W68PBwGcQKN954oxwQduLECTRFTpeRJSIiIqpOlQF7Hk90EViyZImsQuDn5yfXmUwmGbSGFg7kXrFihbweGBgob8fFxRX/8rxjxw65bdu2bdEUMZAlIiIiKmfSAlHvVZTKqmviuOL4VREB6axZs9C6dWsMG2Zth5ubGzZs2IBrr70WBQUFMvMqymytXLmyeD9RfisxMREajQY6nU6OD/L19UVTxECWiIiIqAwx85aYtMCeM3uJrKroA1ue3bt3V7ifyN46CwayTkLj54/8g4dw/sGHEPnZp/ZuDhERUaMng01OJduocbCXk/AcOADhH32E/OPH7d0UIiIiojrBQNaJaIOtU9URERHZW1GR/4p+OifnoRS+B4reE9XBrgVERETU4MQgJTFz1YULF+SIexcbBj/VhsVigdlsrtfHcDSWRnBORBArqiqI17+oZFh1MJAlIiIiu2jRogWSkpJw7ty5es/Mihk9xQh+anznRASxUVFRNdqXgSwRERHZhcjANWvWTGZkRSBbn8GsGMk/cuTIeju+I1rXCM6J6E5Qk0xsEQayREREZPdgpib9I6tL1FWlpnVOONirCTCcO2fvJhARERE1OAayDi7/6FFZG9Zj4ECbtjdnZiLtf4vqvV1ERERE9Y2BrIOz5OXBpVkzRP3fF1Vuq+vWDQG3347Ed99tkLYRERER1ScGsk5ETInnd/MUezeDiIiIqE4wkCUiIiIih8RAloiIiIgcEgNZIiIiInJIDGSJiIiIyCExkCUiIiIih8RAloiIiIgcktNNUbts2TJ50ev19m4KEREREdWC02VkJ0+ejKVLl0Kn09m7KURERERUC04XyBIRERFR08BAloiIiIgcEgNZIiIiInJIDGSJiIiIyCExkCUiIiIih8RA1smo1GrAZMLF5/8DxWi0d3OIiIiIaoyBrJNxCQtDxCf/RebPP8OcnW3v5hARERHVGANZJ+R51VX2bgIRERFRrTGQdWLmzEx7N4GIiIioxhjIOmk/WZfISERPuAHGCxfs3RwiIiKiGmEg64RUWi3arv0Lap2O/WSJiIjIYTGQJSIiIiKHxECWiIiIiBwSA1kiIiIickgMZJ1c2uLFOP/wTBjOn7d3U4iIiIiqRVu9zcnRpehT4KpxhY+rj7yduXyFXHoPGwrXyEg7t46IiIjIdszIOpGzGWcxatko3PjrjfZuChEREVGtMZBtgv44+wfe2PEGUvWppdZnGjJhUkxI06fZrW1EREREdYVdC5qYubvnYsnxJSgwF6BrUFfc0PYGezeJiIiIqF4wI9vELD+5HDN7zUS/Zv3s3RQiIiKiesVAtgm6IuwKqFSqy+/IK+xqoJguuyvtf4ugP3KkAVpHREREVDcYyDoBk8UERVGArATrCnEdQOB998J3wgS0WPK9nK42d8sW+zaUiIiIqBrYR7aJl9p6ZP0j2BS3CXd3vRvDXELkehHGfrrzPWR2yMAT426Ge9JxuLZsYe/mEhEREVULA9kmbN7eeQj2CEbvkN6IzoiGiyZJrjerVPjs2P/k9fxDy5CksuC2+BZoFxEGc2Ym0pf8AI/+/eDRu7ednwERERFRxRjINmEKFNzS4RZ4uHjg7Z1vY7dGh4lZOVjh41W8zU+ebogyGrFNm4Sg+Z8jff7ncr2qczt0/GmlHVtPREREVDn2kW2iegb3RJAuCJ0COxWva+3ZDK+kpuGtpBS8l5QCX7NZrn8uNR3Z4zvj6BPXIrdTJP5tr0JcdpwdW09ERERUNQayTZQowbVxykYMDh9car2oZXBdbh7G5uYVv/hqjSuUxANY2ywGr95cgH+6qABTgV3aTURERGQrBrJO7P3gIXjFvQ0GTF0P6AKwNeM4QjUemJCTWzgkjIiIiKjxYh9ZJ9Devz0CXX3R/cJRwCccyEkC1Fr0H/Uu+rv7ym1u9mgNnZKJicH9kGo+jPwyx8jbtQuGmBj43nADVFq+bYiIiMj+GJE0Iecyz8FUzmQHYpavTd1nAVs+AB76HchLA1RqoDCIFbo374Pu614Fjm7BP+pAiN6zD657EKEeoXh10KuIe/wJmFNToQ0JgddVVzXwMyMiIiK6HLsWNBEGswGTVk1ClHcUQnTWerFSQTZw5GcgOxHQulmD14BWgH+ZurGDnwBeTAbuXg2PdqNggIL9Sfvx06mfrPdbLHKhFC6JiIiI7I2BbBNhUSwoMBfgy9Ffws/d79IdW+cBy6YBa56v+iBaV6DFFWjr3QJtU1VYuK8PopLYV5aIiIgaJwayTZ3ZYF0qFlGewKZdvPt0RHB3QLNlI9749vKuCkRERESNAfvINmX6dCDtLNDzDiBqABDRz6bdVC5aBLW9gPwIF5h+CcbD6x/G3YZseNZ7g4mIiIicLCObnp6O6dOnIzIy0t5NaVzWvgLE7gDajwF63wWEXJocoVJhvYDWQ4GOI2HSADMf3gDPXGZmiYiIqHFpdIHso48+ipYtW0KlUuHw4cOl7jt16hQGDRqE9u3bo3///jh69Khc7+/vj4ULF6JDhw52anUjJSY1GDAD6Hx99fbziwTu+hXhE97B6VuykTT5Kvx5pQ6myND6aikRERGR4weykyZNwj///IMWLcqMqgcwY8YM3H///Th58iSeeeYZmYWl+iO+TEw05+Dq1xfg1K0DcKEgCdGZZ3nKiYiIqFFQKYrSKIeli6zsb7/9hq5du8rbSUlJMhObkpICrVYL0ezmzZtjx44dclth5MiRWLduXbnHmzt3rrwUycjIwIoVKxro2QD5+flwd3ev8+O6nTmDwMXfIebl5/F84vOYHTIbzfMS4Jd9EmHJW5AY0A/RETfV6NjuBcm4eu/DiA8cBFdTNvb9nImEcSMR3Gtsoz4njoznhOeE7xF+bvi3hH9f+W/OJSJpGRcXhwopjVSLFi2UQ4cOFd/evXu30qlTp1Lb9OvXT9m8ebO8PmPGDCU8PFwuT58+XeXxxbYNafXq1fVy3Nxdu5RTw0coeqNe6fpNVyVdn64onwxQlP8bqSg/3KEoCUdqfvD0GEV5xaf4sv7q7sr2ZZ80+nPiyHhOeE74HuHnhn9L+PeV/+bYHq85VNUC8VN3SSWTyZ9//rkdWtRIiVJbQ58F2o6s80NbYEG+KR/uWmZSiYiIyL4aXR/ZioiKBCK1bDKZioPY8+fPIyoqyt5Na7p0/kCz7tYqBi6egGLG5wc+xzPP9MGhAX1xfuZMFJyNtncriYiIyEk5TCAbEhKCXr16YfHixfK26N8q+sYW9Y+leuDmDTzwN3D/JuDKRxFiNOKOjFYYnhwCfYAHjDGxSPrgA556IiIisotGF8g+/PDDiIiIkNlXMXirbdu2xfctWLBAXsSgr7fffluW3KIGMvQ5NOvkici1Z9BhVwJS+rWB3623ABYLXwIiIiKyi0bXR3b+/PnyUh5RJ3b79u21Ov6yZcvkRa/X1+o4ziiorw4+dz6J77f9ieQrWqL/RQaxREREZD+NLiNb3yZPnoylS5dCp9PZuykOydVHgWrCaCw+/xP+jvu7eL05OxuWvDy7to2IiIici9MFslQLoV2AlY9gqjoQ07tOR64xR642xMbi1JWDceaacTy9RERE1GAYyDq5hMx87DibWqqUWYVu+gJoMxxIO4tQjQ77kvbjdMZpmDMyoBgMMCUmNkSTiYiIiBpnH1mqpVNrgbwUmzefsXgPDpzPwLxbe+H6HmFV7xDYBlj7EqYA8PEYBPW5WGz5+ROwCBoRERE1NGZkm5rfnwJaDwUi+l12l9miYPGOGKw7eilzqjeYSi2rdM27wEupUA1+EkO7RgEtwhG15FJfWSIiIqKG4nSBrKhYMGXKlKZdtWDILMDd97LV+8+n48VfDuPB7/bU/NhidjWNFnDxgO7CGijXd0BisAviOwbXrs1ERERE1eR0gawzVy0o6gYrlgaTBflGc/F9K/ZewLmUXNsPNmgm0OVGmC1n8Mi9Cl4bkVYPLSYiIiKqmNMFsgSYLAqGvrcRfd9Yh/jMfDw6vC2y9EasOnDR9tPjogNCOvN0EhERkd0wkG0CLIoFd/xxB1zVrtDChuoDAC5m5iOnwITsfBN6tfBHl7BLXRGSswtKZWsrZbTWjjVoAZMaiJ44ifVkiYiIqEEwkHVgitmMvN27ZSB7JuMMfp7wM7yqiGO93bXo2Mwb3cJ90am5D1y1aoT5XupmsfpwPPrNWYdHluyrugEhnaFJj5FXsz1UmHWvBvlHjsCcba0vS0RERFSfWH7LgWWvX4/UL/4P2psnQKX6BVE+FRfBOpWYjcd+2I9mPu5Y/fhVcp3Fosj8rUatKt4uMatALpOy8qtuQMsrMWTsx3jz9xlwVxTMCg6qi6dFREREZBMGso7MZIJb+/bQPHg38Osv5W6SklOATzacxobjSTIb+/Xdl8pyqUsEsMKFDL3M0FaHRqXG+Nw8JGg0NXwSRERERDXDQLaJ++qfaKw9mogxXZrhzitaoHmJbgQl9W3pj+d/OiSvi4C3ujwtFgSbrP1qz7/0H7h6eKPZSy9CGxhYy2dAREREVD6tM9aRFZcmXUe2BIsCjOgUgpfHV15h4Nb+UZjSNxInE7NxLD4L3247V63H8VYUrIm7iAU9wtH5/GE0TzRCf+AgvIcPq+UzICIiIiqf0w32cqY6sr/su4Dle87DVWPbyyz6yooBYB6uNesmoFUBfT76Cs/docASHgKlIB8FZ6OhWCw1Oh4RERFRZZwukG3S9nwLmC5lmrecTMbgtkF4dGS7+ntMjwDr0s1avmtg84Fw17rL6/GvzsbZceOQ/t339ff4RERE5LQYyDYlqx4F+t8PBF4KXNuFesPH3aX+HrPFIODRfcA9q623f51ZfJclM1MujWmp9ff4RERE5LQYyDY1Q2YBWtdaHSLA0w0HL2Si7X/+wNiPttiwQ2sgtDNwy/fAvkUQHROO+eTApFUhwQ84dm4nFKOxVm0iIiIiKouBLF2mf6sAbHtuOL6/byCOJ2Tj6vc2otura5CQWUVt2fZj5eKjAa/g8xvdcdvTGqzpo0bI6n1I/mQ+zzQRERHVKQayVC5RpivMz9rXNSY1T05lK2rS2qJ7QCf4ufnJ67/3V+PcjX1hZvcCIiIiqmNOV36LbOfpqoWXmxZi3oR8Y/UqDzzS6xHsStiFc1nngBOZMMYn4OJ/XoDnFVfAd/x1fBmIiIio1piRpQr5e7pi63PD8c9zw+FVnUkSTq3BVRkpmNX7CQS4W6sa5P7zDzJ/+gmpX3/FM05ERER1wukyss4yIcLiHTHYfDIZHZp51+o4vrrqVDxQAVGDgDUvAoZsAHcDbXsC5rBatYGIiIioPE6XkXWWCRH+PByP0V2a4a4rWtbJ8TzdNHhg8R6cS8mteCO1GrjnT+A/ccB/4oHxHwN5aUgNy4Xn4MHwGXcNYDTB0sS/RBAREVHDcLpA1pn0jvKDroazdJX14/1XyJm/zqbk2LaDqwfQZxrg3Qx/hWSj4L1nEHjvvSg4dw4xd9xZJ20iIiIi58ZAtgnKN5phMNXttLBhfjroXKofFN/s0xHG7Hh8s/k/2KSLhdtn78CYmFinbSMiIiLnxEC2CZr+7S7siUlHVIBHnR9blOJSFMXm7TuPeR+Dfdvht8zjeHrL05h/eEHxfR579iDt22+hmM113k4iIiJq+pxusJczSMwqwMJp/TCgdWCdHrdPC3+8+ccxBHu74bruNg7g0rqhk2cE/AouoLlHM5izrZMq5O3dh6D/LYLIzep69YKue/c6bSsRERE1fczINlFqlarOjznnxm64ok2QnByhOkZ4RGDzuVjMOLsXlpx4mFNSEHPbbdjfVotcN+BizsU6bysRERE1fQxkqVoCPFzwyq9H8Oeh+OrtaDEi2GTGllAzfp/SAuHzP8GbkwGDFsgzsooBERERVR+7FlC1vHVTd+iNZpxIzMY13ZpXa9+uBgO+MnpjertE5LhuvHSH2bapb4mIiIhKYka2iflxVywSs6z9UOuDKOfl416dSRLEiK/rgc43AAMeRK+Ek5itBGHF2V8v3Z9VzewuERERkTMGsmJWrylTpjTZmb3+u/E0pvSNxMDW1qlhG4Vm3YAp3wLXvA3VDZ9jYMyeUncfyYrG/P3zkVmQabcmEhERkeNxukDWGWb2mtw3Am7aupkIoTxuLmos3hGL3w/WIJMa2OayVT9c2IDPD3yO1dGr66aBRERE5BTYR5aq7enRHZGeZ8RH607C39MFg9oE2b6zzg/QVLNrAhEREVE5nC4jS7Xn6+GC56/piGa+7vjgr5NIzq7GYK2A1sD9m8u9S715B/ZdPxoXVi7ny0RERERVYiBLNRLh74EnRrVHbFoeZn6/t1r76nzC4efmBw+VB1Ql6t0mbFwD95PnsX/VV3xViIiIqEoMZKnGekf5440buuJMci76z1mHa+f9bdN+3q7eWDtpLV4KeQkqXApkiya+VfTpfFWIiIioSuwjS7WWkmPtWpBUjS4G7lp3uKgq6CtrMvBVISIioioxI0t2pypKxRaKtegxb+88WY7rgbUP4Pm/n4eilNmIiIiInB4DWaqVcD8dXDVqRAV41Gj/TG81Zn9nxksBd6B3SG+5roPGC3sS9+Bk+knsivkHq0+uglkx85UiIiKiUhjINgWGHMBsBNQuMKP+6seWp2u4L068MRZL7h9Yo/3nTg/AqTCgy+wf0fxshlwXrHbF0dSjeO2XR/Dlx2bMW2CGYmYgS0RERKUxkG0K9GnWoVIzNiMHNcuM1oaoPHBpyFb1GFzVeG+iBvmDesBw+gxcAjwRaNLgw24vwzU1B+5GIChLPD12LSAiIqLSGMg2JaFd4GhGRo1Em8geCH3jNXQ4eADBE3oh60ASAm9+Bq+scrd384iIiKgRY9UCqlNf/n0W91zZCmq1bTnaFwa+UOq2b//W8FHWIyctBHErM/nqEBERUYWcLiO7bNkyTJkyBXq93t5NaVKa+bjj6TEd8Mbvx5CcU4DtZ1Ixb/0pZOqN1TtQ37uhGng/VIEtrLc11rfo7O2z8cC6B5CRb+1HS0REROR0gezkyZOxdOlS6HQ6ezelSREZ2AeublN8+/XfjmLu2pP47eDF6h3Iuxkw9Dl43j0bESON8L3WOnht1dlV2HphK2KyY+q66UREROSgnC6Qbcoy8gywWOw/KGr+xtMoMFmrDPx5KAHn0/KqfQxVSEd4z9kMrc7aveDmLRY88LsZyGMmnYiIiKwYyDYhQ9/fBHcXDQI8Xe3y+Bq1Ch9M7oFlu+PktLX3DWmFxKx8LNsTV7MD+oYDt34vr964XcHwgwpU0TU8FhERETU5HOzVhGTkGbHvpVHwt1MgK0zsEwGVCjiRmI1HhrdDlt5Uq+O5BgXjm1EqqBUNxm834bsDC7Dt/BzMHzEfg8IG1Vm7iYiIyPEwI0t17qbeEXj+mk7wcqv99yQ/N188GJqI+1/7GSq1GvG58Ri50wDdXc8i5++/66S9RERE5JgYyFKj19JkQqsSM5Z1O6fAPS4Febv32LVdREREZF8MZJuAPWlH0ZidSMjC4z/sw/pjicXrFEXBF0fMmLJgO9JyDRXv7OYFBHcC5g8oXuWidpHLs5ln6rfhRERE1KgxkHVwuaY8vHbwU4zPbbyj+dccScSqg/F4/Mf9sqJBkT1JCnZGpyG2sqoGrp7AwzsAswG+rt548UBr9IrVwOSihnbzLuRs3dowT4KIiIgaHQayDk5kNkPcAvBqWhYaIzHwS7ipVzjuHNgCfxyKr/GxXAx50B04hcAp47Dm+d646GtG7Irv6q6xRERE5FAYyFK9mjm8LV66rjMeH9UevaL8cSFDjx92xtbsYKZ8uXA7+xVuiP4T8c2Bi7FHYc7gbF9ERETOiIEs1asIfw9MH9wK4X46DGkXhPHdw/D26uO4mGkNSmvEYkZboxF+LYMReC4d8S+/UpdNJiIiIgfBQJYajJis4ZERbeGrc8GYD7dUf39/I9SuFrh6W2vTprf3wf6busKSm1sPrSUiIqLGjoEsNagQb3csnXEFcgqqOVGCSoXIq9LQ4eFQuDf3BoI6yNWKWoX8o0eR/j37yhIRETkbzuxFjuHWH4GcRKDn7YBaDfz2BLTGBMwPOoCsVmb0/+wtDLrtdnu3koiIiBoQA1lqcIGernhoaBucPXsWO1OtNWGr1H70Zatu82qHEO9I5IcuhSHWQ1ZwUBWVSSAiIqImz+m6FixbtgxTpkyBXt946642dVqNGs+M7Ygb22igUV8KPJfsjEWf19di0Y4Ym47jodbiev8uiNAaEJyqIObW26BYLPXYciIiImpMnC4jO3nyZHmJiIiwd1Oo0NLd57H5RDKOJ2QhNdeAQ3GinFaLys+PSg0c+QVo1g2mIDM+mabFo98fxclRI+E7YiSgACFPPA61hwfPMxERURPldBlZalym9I3AyYRs/HfDKfx5OAFBXm627Tj4SSCgNXD0F3lzVygwY4aC00oSMpYtR/rixcg/fqJ+G09ERER25XQZWWpcnh7TUS6NZgsy8oyyW0FCpg3dPnzDgcj+wOm18maBCijwVPDcVBWGRlyJ21/YjMyCDDAfS0RE1HQxI0uNgotGjWBvN1RrqFan8UDXSfBtN0be9HH1kctNcZtgtBiRlJcsb5uzs5G9bh1M6en10XQiIiKyEway5LhCOgGTFqJH1zvwZ54HFo1bVO5mKZ/MR9zMR5A898MGbyIRERHVHway1CREKGr4uvqWWqdKz5RLxWiwLg3WJRERETUNDGSpSYoJVsHlxbk41rkLcv/dKdeJ0lwsz0VERNR0MJClJumdKRoU3J4Fj+4dYThzBtrgYGStWoWE2a/Zu2lERERUR1i1gBqdEwnZsqZsx2bWwVs2yTgPfNIPCPG8tM7LEyE3X42sPgPhf8styNm4CTmbNtZLm4mIiKjhMSNLjco13ZqJuQzw9p/Hbd8pqB2g8wc8Akqt/j8/b+S1CsA/E1pj2D+34N/Mg3XfYCIiIrIbBrLUqIgs7GMj2mHTiWS0f+FP3LnwX2TmGSvfKaAV8MQh4Obvi1fN7vEoTqhM2JN9DsfSjiHLkIWLuReL77fo9bAUFNTnUyEiIqJ6xkCWGp0RnUKx8z8j8OOMgTh8IRPDPtgEs0XkaW03ILgHQhQ1EgxZxesS8xKRk5cJw/nzOHXlYJwZPQaKsYogmYiIiBot9pGlRinEx11e1jxxFfrPWQ9FEYFs5dMlBLj54bXkVJhUQJguBF0ULT68sAbuWg8MjxyO3IxzwIGjiLntdljy8uRFVDGo1iQMRERE1GgwI0tNhsrNCzd6tsJktwioPALwgtkbV/l2QI4xB618W6HvoGH44QYVTMnWGb/KSshNwK+nf5XdEIiIiKjxYyBLTYerB/DgVmDmTsC9sOJB1gXr0pCD9ifWI1lTpovCjs8Ai0VefX/3+3hx64v4+vDXDd1yIiIiqgEGstR0DXkKbvnW7Kpb0nF0Sb+IaT6dS2+z7hUgI0ZeNVvMpZbIigcMeQ3caCIiIrIVA1lqujqOw3NenTE//FpM8+0CRPQDQksHsk8FB2HW7nes3QnSz1lXpkcDp9cBczsC30+xT9uJiIioSgxkmwjrYKima8XeOMzfeBpGs7UbgK0CXDxx1Z4foDv5l7ythLWVS5XWepwstRpbEndhdfRqxOXEWXfKjANykqzXcxLr9HkQERFR3WEg2wSYFQVGs4LrujeHl3vTKkThqlHDRaPCsysO4b01J3DoQmb1DjD2HSCiP3BxL+DXAqbObTBrhguemmY9T/MTk2S1g9d3vA6VAgzIyweM+YAp/7JD/Rv/L1adWQWLUr1gmoiIiOpH04p6nJRIxooSUp/c1htNjZ+HK/587CrxLHH7l//ig79OIDGrAO9P7oGekX5VH8ArGLjtR8CYB2h16GzIQMdO/RB0aAMAd7iIbfJS5Ql8crMGvtu0ODTsFBDz6mWHmrl+JvLN+Qj3Ckfv0KZ3romIiBwNM7LU6LUN8ULbEG95fevpVJxOysHBuAzbD6BSAa6egFqNAPcAzL1yDp7JtQ7o0vd+FyqVGmqLAu1Fa1kuT3M4kH8p87v5/GbsSthVnIllRpaIiKhxYEaWnI93M2ieOwmvxCcR+5+P8VygBqEpIrB1vWzTs/oUzNwwEyqooKBp90MmIiJyNE6XkV22bBmmTJkCvV5v76aQHanc3RH52aeAVovQFBO+GaFGypA8nOsXgFSzHjHZbjj1VyTyj7WU2xcFsRqVBm/vfBuxWbHFx8osyITJYrLbcyEiInJWThfITp48GUuXLoVOp7N3U6gROdNchdz2BoR7hyDPYsQCSyBMaWYkniw9C9hHwz5CgbkAR1KPyOD1u2PfYfAPg/HKtlfqp2GGXGD189aJG4iIiMi5A1lyfAGernj51yP4aW9huaw64jLmLYQ07402rn6yLJdgUgonRyjUetkM+Gs95PUvD32J93a9hza+bRCfG1+7B8+IBc5uso7cK+niPmDHp8C6ywefEREROTsGsuQwdC4auXxnYnfc0DMMZ5Jz6uzYT/R5HMO63SU6HZRaL/rGhqhK9J01ZAOmAnk1oyADd3S8DdNDrwTMxto1YMmtwP8mIDTt39odh4iIyIkwkCWHsWj6APx4/0CM7BQCTzct4jPykVtQN31Te4X0glp1+cehhU8LLJn056UVHoFysTZmLTbEboA68QiwdS6QcrLmD77oRiDpGODiiYjEdUDG+Zofi4iIyIkwkCWHERnggQGtA6FSqdCnhT/WHkuUkyTUJbWPN8K2JuKa3dZSWyK4ddO4XbadCGS7uvjj7qObALUrYDEXz7Am+s2KbgfmwnVViv3XWuv2th/hbkgFDq+o0+dERETUVDGQbQJUFgOczU29I3DfkNbIqWVG1nv4cLh36waXyEh5O/jBB+E7uANai5lp3VQwxMQg5b6Zl3bwCMKMUztxZ+QYPKdpBv82I4BuE4vvzjZmy6oGH+/9GLHZlyobVMkvCmg1BNkeLYGTa4BPBwF7vrHWs1VpALMB+G4KYHK+15qIiKgiDGQdnVEPtT4Vfyn97d2SRmPxjhi8v+YE8o1VZ0QjPv4IrZYthUtIiLytcnVFs0nDENQ1Cy3v6oDIzz+H8eixSzvc+Bmu9GuPZ4IHIkTjDng3lzOGiSFa62PW43DK4VLHn7NjDvot7octcVtsfwKx24CkI8D614AfbgNaDwXuWgmcWgMYc+Um7+x8B30X98XG2I22H5eIiKiJ4YQIDk/MT6vBk+ZHcZ29m9JIvLLyCMwWBcM6BqNPi4Bq768dPhPBXa4G/FshPzah1H2W1BzkxSvQKUqpYWF7UYDdmx6HWgFu3GaBu1GBcp0JJ9NPymltz2acxVURYqrdahBT50YOAO5YYZ1itwRxXFEG7EzmGQzDsGo/RyIioqaAGVlyaKJa1Z6YNCRl5192X0qOAbvPpcFiqeaMXKL0VvMegLtP8Sp3jTu0Ki0MT76MmCVJyNlzqW9upJs/RD2Flj4t4V6g4NYtFty4XYFyoXQQbAuTtrC+sYtn4RqVdYrdKpxIO4ERy0bgmS3PVPsxiYiIHBUDWXJovx28iImfbceMRXsuu+/RJfsw6fPtWL6ndvVmRQmuX2/4FStvWAl1vrXMVuqPf+L0K78ha/8F9PSMwF60wMfDPi69477vAX269XpBpk2PdaLFXcD0tcBVs0qtP5t1Due1Ff+AEp0VjaS8JBxIOlDt50dEROSo2LWAHFqByVpdIK/AjJjUXLhpNZfdl2uofYmuMK8wuTxdeFt/Lk0u0/flwr1nGkpUmr3kyAr0T/fD5FMa5E7+B6dbX4sT6SfQPbg7Ir0jgbSzgLr0R9As+t1G9gfSoq0rPIOQkJuAm/64Hdrw5tiydCq+6ny1nFmsvHJhREREzoSBLDksdeEv7q4aNU4kZuPq9zbB200r+8c2BO8BnWFIy0fysi0Ia7kXgV+9iadCbwBQVD5LjXZn1YhMAJYdPYk3Vt4os7v9m/XHlwNnA5/0AzSul/V/lbpPAaIGAJ4hKMhPhlkxw6xWwRi3C2s8CzC1y1REZxQGu0RERE6KgSw5rDsHtoS3uwuubBuIxKwCaNUq3PzFjgZ7fN3Q6+BuMqHg+HHom92Ki+//ga4l+rMe8guGB6xdEfIVE+7sPBWdAztj2YllSMm+ADfFDO8yQexu/W4sXLUQD/d8GFdHXm1dWbL7r8ZFLnoE90BMVoy8LiolfLznY2hUGuSZ8vDAugfgqnbFB1d/AJfC7YmIiJoi/jZJDsvXwwVTB7VE2xBvXNk2CG1CvGzaT0xasOrARWw6kVStx7Pk5UExlyzpVRS0qqA071N08OJ7v3RTkFPOQK1T6acwct10zE1thsxzhYO7Cp0oOIFjacfw29nfkFdeprYc+5L2IUAXgDcGvyGnzd16YSs2nt+I3MJSXURERE0VM7LkdE4m5uCRJfvk9RNvjC3Vr7Y8IniNnz0bGUt+kLc1gYEwp6aW3iig1WX7dUxV4K0vsSLlFLDpQ2QHB+LqQwqm/KXCRfjD67YnoAlsW7xZp4BOsu7sYxsfkxnWg8kH4efmJ4PUioR6hKK5Z3NbTwEREVGTwIwsOYUjFzMx9aud+GFnLIxm6yCwMglUnE3OwcJ/opGcXVBqX0Wvl0Fs2LvvoOOhg3Bt0aL4Po2PL7L++gtxMx+Ba9s28Bw8GN5jxgAuLpj2swUWNXA+1JqVbX58NVyNkVj6thkP/27Btk7Wj58y8EFAfSmYHttqLB7q+RB2xO9ARn4GFoxagGXjl8n7fvFwRaqogFBggHdSrnwCDZ15NWdlwRgf36CPSUREVB5mZKnJ8HTVol2IF0wWBSnZBcguMX3txuNJ2HwyWV7C/S79nP/emhN47pqOcNGo5fU/DyfImrTPX9PpsuN7jxwJlUvpPqd+kydB17OHDCi1oaHQ+vvL9cd79oLGaMRXo9SYdTIMD3l6ITxzAzYXtMHZ0GwsvDcCY9tdB0z7HLM2z0I8MjFv+LzLHjNQF4hBYYOKuxm876vDzWFXIuLl93DTrrNYfP12rOxSgHu63iPr2A5oPgAB7gH4M/pP/HDiB6Tnp+ORXo/Ay9W2bhe2OHfbbTCcPoOWP/4AXY8edXZcIiKi6mIg66BMqanI/OMPezejUdG5arD2SesAqSU7Y3EwLhNnknOwYPNZ5JvMuLV/FIa0C8JD3+2Ft7sWr4zvgmeWH8DUK1oiKtDjUna2GkUPVGo13Dt0qHwbd3/oVApy490QvC0JsS1VKPB0xa0db0UcPodm3TZ00gPnup+y6TFv2fYtlBgxWYMbVFl63NzhVjze+3GoVCp8OfpL5JvyZSA7f/98ub2YUezK8CtRV8yp1tJjpvTCGrlERER2wkDWQWWuWoWCEydhvPUKAD/buzmNjghab+0PnE7KkYO6grzcMLRDMPaft/YzFT/2T+oTged/Olj/jRHdBnReKIjRIr+VD/57XQ5CStx93xprV4ec6ysOZD1cPPD2kLeRm5OI1gPDcH7vx0DSOXmfqFYgglgiIiJnw0DWgbl36oTMPm2Byye1okJtQ7zkpYjORVOcva2MmNr2ti//xdgWnpjVrx/U3t5QuZY77YFtIgdACe0O876tyHc7Yl2n1sCgAbRmyL60Xx36Chej3Cs8xLWtr710w0sMPLMGskRERM6KgSw5lQGtA7HsgSsQ7OVW6XbnUvNgMFlwOMuCo8+8A083LSIrmSK2Il2DusIv2Q/pP/wIY1wczDdeVXyfWueOp6dr4GIG3lkThL5nTeh48CLcmmfCFBqAhmBMTARUKriEhCDrzz+Rt2sXgh58ENrg4AZ5fCIiotpgIEtOp19L24PEs8m5ePC7vfL6sdfGykxu8OOPIXfbNvheP77K/Z/sMwt6zX6kbt2J0Jdfwpl+nsC2bcX3xwdauwQoFjUG/W0t6RWz7TgSbxxkU/uaazzRrYb9X00pKTgzajSg1aLd5k1I+fQzFJw6BffOneE3aVKNjklERNSQGMiS08vKN2LO70fxz+kUkZyskFI4Csyzf395qYza3R3m/HyZdQ2acb+8SGd/K3f7xKx8hAOyq0HYyl1o9Xc0XhzaF+HD70b6Dz8g5fMFcGvdGpGff1aqi8PNvh0RFHEpyyu4adzwbL9nkW3Ixu/Rv1fYRkt+PhSDATAYrEsiIiIHw0CWnN7tX/6LTL0RDw1tg/S8ugnoWv6wRI7q13XvXmq9r6uvdelmXZa1aLgaV1xxHQadUOPKxEz4X3BF7Jw34XXVVchZvx4WvR6akn11C3IAsxFIPQ34hAPuPnLg1x2d75B3rzm3pnhTi2JBUl4SAt0DoTFZANOl8mQlKUbrtLrF+xUUQKXRQFWDrhVERET1if8ykVMb0i4YF9L1WPP4VWjm6465f53A51vOom1w7equurZsKS+XPV7EEPww7id8tzUNi7ZfGqyV7OGD5rnpOB8MdO7eEq4FaqR9/TWy//oLHn36IPy9d3Gid+E0uCUYju5DwpSe8AlNgseVw4Dbl1bYphVr5+HCN18grG1PdFtxENrQkrUTrEQt3ITZryF96TKEvTkHismEc7fcCl2vnmi5eHGtzgkREVFdYyBLTu2raf1K3Z45vJ3Mzn67PaZOjm+xKHKCBlftpUn0MrP88f2Ok9C5xkPbxrrulSGT0LfLOhw1HkHrxBx8sSUZNyQlwb17d0Qu/LLC7GlmtKhyYIEp0xMevcuv65qab+1767F2B8bsVYC91ul5TRcvzc5ljE+Qy2avvAKNtxeS3v8AKQu+gPfwYYDZDOP5OOt2SUlyyl4iIqLGgIEsUQki4AzxKb8E1p6YdGTnm3BN12Y21219dsVBLNsTh8/v6IOxXZtBURTEpOaV6nMrmDQaJBXePp+Wi+DCaXLFz/lqV1dYKghkq9Levz1e/OfF4i4NlxETOnTqhHO33iofS6XVwKNfPwRMm4qLzz2P3K1bizcV2dmz114HjShFVmaGMyIiInu4lCYiIqlruC9CvN3QM9Kv1Bm5c+FOOSvY3ljbZ7QqClpj03LlcsPxJLz862EM6xAMrfrSx29kpxAkpluDQ3d16cctr2SWOSvLpsd/9+p30b95f6TlW2fjKiIyvWHvvoOohV+i1Yrl8B42DNqQ4OLuED7XXIOQp5+GJTv70k6KIm+LqWm1QUFyVfaGjYiZdjdyNm+27YQQERHVIWZkicq4un0wdr4wEn+fSpbBa1kmczXmsC1DZHR7Rfrj67v7IyYtDdetsq6/dUAU0tfeh4T489iWF4hOwRZk+IciaOCAy44R9+BDMGdkwK1dO1kuS6okQyyqGHx24DPcnGtE66KVKsD3+uuLt4mY93HNns/6dcjbsQOuUVHwuto6PTAREVFDYUaWqJZ9YI9czJQlvKrL080VisUFiqKWwaYarjif4Y+JvSMxfMoYLJ45F8GPPnr5Y+r1aP7G63Dr2LHwU6xCXrof0vaUyJ6W8MoVryDCOwJJeYmwFMa7Km3tugaIbgjJH34Ew5mztToOERFRbTCQJaqCu4saN/UOx7XdmiOozIxgS3bF4tp5/+CppQdwIUOPjGqU73LXuiP37BPyEqJrXrw+3F8HtxKDwyri0bcvXKKiEPbWW/C5qheyTujL3S7Q6IZu8a5QK8DG7iqsu6c7wua8gdoIe+9dudTv31+r4xAREdUGA1miKrio1Zg7pSfm394bOlc1jidky0FbQpbeOghr+9lUDH5nA8Z/8k+Fx1l/LBFPLTuAQC9rHVh3rRrjOnbB1a06o5mvDqO7NENLb2BAK9tmHvO/eQra/rUGvhMmwKNjiwq3u/jCCxj79mZct0+DtiGdcKZf83JLg1WHmJxBG3J5+S4iIqKGxD6yRNUwtH0I3vzjmOySeufAFqX6vgppORVnZE8n5WBIuyAZFMsPn0Ytg+Mi0we3QkTuSfSK8pcVEsojarxacq0Dx8qymBR5n9rTE+bMTFllQBsYWLy92mCCTushr1/IuQAfVx94u3rb9LzFsRLfsWZhK53+jIiIqAExI0tUDa/f0BW3D2iB1387iuV7rLVVi2jVKuiNZtz77W6YzBbkG80wmC3yvtwCsyyu5emmhc5VU/0PqocHwj/6CFl//glTUhJcml/qiiC4NAuCIcWAmPGDofz2LM6MGYvTw4bDePFiqe00KjXWx67H2BVjMX3NdJsf35yaivTFixH23nvFFQuIiIgcLiObm5uLrVu3Ii4uDjqdDj169EDnzp3rp3VEdhTq4y7rykYFWrOYRV4e3xmJWfly4oSSogI88NqErrhj4b/IN1kw/ZtdOBiXIbsKfLz+lCzp1d/GbgPl8Rk7Bj5jj5Z7n+7aGYjS5+HivKVQdi6EOSNYrk9b/F2pKWdb+7XGpyPuRa4pFx/s/qB6DVCp4Dv+uhq3n4iIyG6BbExMDF599VX88ccf6Nq1K5o1a4b8/Hy8/vrrsjj8U089hbvvvhv2UFBQgBkzZsDHxwdmsxnz58+3SzuoaWkf6o3Dr44RRQFs1i3COvGACHRFDdlv7+mPwW2DMHvVUXyz7dKUtNUhsrufbDwNdxcNZlzVuvzJGDRaoM0wwOW34lVBDz2EtEWLStWCVUMtp8ndn8RBWkRE5ERdC+68805MmDABFy5cwPr16/Hdd99hxYoVOHr0KH7//XecPHkSn3zySa0b9Oijj6Jly5byH+vDhw+Xuu/UqVMYNGgQ2rdvj/79+8vHFkQ7rr76asybNw/+/v7Yvn17rdtBJIiMrOjLWl0jPtgsM7atgjzle3lMl2ayf6yofFBd59P1+GjdKbz95/HiwWW28L/zDmhD62BAltb6fVflah2kVp78Y8cQPXESkj/9tPaPR0REVNcZ2S1btlR4nwg833rrLdSFSZMm4ZlnnsHgwYMvu09kXe+//35MmzYNy5cvx/Tp02XQKrLFAwcOlNu0bt1a3r7iiivqpD1EFVl9OAHRKbmIDNDhfJoezf3c4atzwXuTusv+saIvrbownXtFm0B5aWie/QdAyS+AWueOglOna3QMMYVtyKxZ0Pj5VhzIHjokl4rZjMB775XT6hIRETld1YKrrrqq3PVJSUnYu3cv/vrrL3l74sSJmDlzJs6dO4cWLVrI4FUQt6+55prL9p87d668FMnIyMCaNWvQUEQ3jLp8PO/jJ+CWmIijx47JUlAWi6VBn09jPCcNKSHBjD3JCnoHq3BnBzGQSwNXjfU95SP6swJYu/ZEjc/J8VgLTqUqWL5qNfJKJGHXb1gPT5fy+zq4nTmDoPx8pJ+11rrduHEjLAP6AwP6I3DxYniKqXLPx+LAmjU4ZziHfH3l59/r2DGIHr2ZmZk4mZkBiEvh50wITkmGrsT2Zh8f5J88iYPjr0fik0+grjjy+6Q+8HzwnPB9ws+OPf6eJJmScNF4Ed3cu0Gjqv6g5UYVyEZHR6NVq1ZoSOfPn0dYWBi0RT9zqlSIiopCbGysDGoffPBBHDx4EAaDodxs7JNPPikvRSIiIjBmzJgGa794o9Tl46XGx0Oflwd06gTVnt+gVqsb9Pk0xnPSkOK9opG0NRr3je6Ea2rQXaCqc+J3NhWbvt+Llcm+mH19F+DfTfL+EcNHwNej/Fm58o8eRfS8/yJptw8CZ8xAxwkToNJY/9hc3LABmWJAWmQU+o8ZI/vILt+yvNLzn5acgkQAvr6+6FnOdrHLVyAXx+E9erTM+PqMGydLf1144sk6fV0d+X1SH3g+eE74PuFnxx5/T6asmoJjmcfwRtc3MK7tODh0IPvee+/hUzv0hSs7yKWoKL2bmxu++uqrBm8POa9pV7aSl/oyoHUgXrquM+auPSkrH9jCvXNntPpmHtQ/3Q7XJx5HQ3Fr3x7BMx+W1/UHD8qlJT9fLtXu7g3WDiIiqj8mxfrzoMli+1iNRhPIip/uO3ToUBw8njhxosED2cjISFnyyySKvGu1sh0iSyuyskRNUd+WAXJK3Md+sL3CgHv7NoCXtXZtbbmEWTPNLmFh1drPnJ2N01cPleW62qz9Cxpv2yZdICIiqi6bhmOPGjVK9k0Vl7Vr1+Laa69FQwsJCUGvXr2wePHi4koFYpCZuBA1ReF+Ojw9xvoFskiB2dxgj+89fDja796F8A/er9Z+Sn6+nFnMnJEBS56+3tpHRERkUyD7/vul/yH77LPP6u3MPfzww7L/qsi+jhw5Em3bti2+b8GCBfIiym+9/fbbWLhwYb21g6gx6BruK6fCFQFtsLcbhr+/GY/9sA9jP9qCEwmX6sMW07iKuWqBJbcCpoqny7WVxssLqsJ+6URERA4ZyPr5+cllycFS9UVMZlDUhSAhIQGnT18qGSS6N4hyW6Jm7e7du9GlS5dqH3/ZsmWYMmUK9PqmkSlKyM6H2aKgR2TFpZHIcXm5aeW0uA8Pa4u/nxkm1/26/yKOJ2Tj0AUxfKsM3whg6krgxB+AIafhG0xERE2SR64JrRIU0ccUjUm1Kr3v3+/4swFNnjwZS5culdPrNgX5ButPzcseGGTvplA9EzN72TTJWEQ/6/KvlwBjxV/YRMm2VH0q7l59N25aeRPS89PrrK1ERNS03P5tHN752gzvrUfQmFR/yiJqVM4XJNi7CdTYuOiAKYuAw8uBtLNylcrDQy7VntZloHsgsgxZGLp0KKIzoxGTGYOkvKQ6bYZKp8P5++6DKTW1To9LREQNzz3fOpBYk2etStNYsPObA0vWJ+N/yVswqvDNRU1fkLcbcg0m2eWgUp2vB1bOLL4ZMusp+FxzDXTdusnbkT6R2HrrVlgUCzRqDYYttXZbqEutf/4J0VNuhvFiPLSBDT+rGRERNX3VCmSL6rZS42C2mNHKLRyPpVZ/BilyTL/OvFJ2J6lOSS5B4+UJz/79S61zFQPDSsg15iIuOw4R3hF10lbXli2hcil/8gYiInIwlsKqOY1s/EW1uhb88MMP9dcSIqqSj7sLQnzqZ5KBRzc+imt+ugYbYzfylSAiotKUwkA2W8z56KCBbGhoKBxdU6taQFQhMRf26ueBnGSbTlJmgbUKQnoBB30REZFjqPZgr0WLFiE721q/UpTIcrTuBk2tagFRhW77EUg+ASQc4EkiIqImqdqB7Jw5c+Dl5SUzmmKygmbNmmHr1q310zoiqrnI/oDOWgO6Prh3aA+Vuzvc2rWrdLucv7dAacAZyYiIyHlUO5D19PSESqXCH3/8Iaeu3bFjB55//vn6aR0R1Z741cRY9+VSQp56Ch3374PPmNEVbuN/661I+3Ihcv7+u84fn4iIqNqBrJubG+Lj4+XAr5tvvhmtWrVCbm4uzyRRA4tJzZWzulXptyeBOaHAoeU2Hfdc5jkYLcbaNxBA8MyH4dKihZh9oU6OR0REVKtA9vXXX0evXr1w6tQpDBs2TPaRzclpXKUYiJq6fq0C8H9/n8V3/8ZUvXFmrHWZHl3lpr1DeuPHEz/i+2Pf10EriYiIGtmECCNGjEBCwqXZpE6cOCEDWiJqOE+Oao/Y1FzkFJgq39DV07rU2lay695u92L1udXQm1jVg4iImlBG9t9//y13fYcOHfD555/LwV9Hjx6ty7YRUW3d8j1wz19A5wnV2u1s5llkG6zVSYiIiBw+kH333XcxcuRIfP311zILm5mZKTOzmzZtwjPPPIMBAwaUytQ2VqwjS07FuxkQNUAUlQUSjwA/PwicXl/pLleGXYl/4//Fp/s/xfns87jjjzvwzs53bHo4lat1trDyZvTKWr0G0ZMmyyUREVGDdi1YsWIFdu3ahQULFsgSXHFxcbKCQffu3TFx4kRs27ZNluVyhDqy4hIRUTfTcBI5jCM/W5cFWUDbEaXuclFbA08XjQvGtR6HUxmnkFWQhRNpJ3Ag+QDic+PxbP9nq3wI944dEfX1V1D7+JRarxhNyF6/HvmHD8ul94jhnL6WiIgato9sv3795IWImpbPR34ug9UBzUT2tnY8r7jisnUJb7wOc3IKPAddgaxVq6DW6dD8tdm1fiwiInJu1R7sRURNT4eADvJSX0QQG/rSiwi4/XakLf5OTpIg5G7bBrWPL3Rdu9TbYxMRUdPFQJaI6pXP6FHIdnWBR1/rrzkqrUYu80+cQOw90wEXF3Q8sB8qdbWrARIRkZNjIEvkDFx01qXGrcEfOujBB+WlLMVQOOmCsW4mXyAiIudToxRIUlIS/i6cctJkMsFgMNR1u4ioLo2aDdz5CzC06gFbRERETTaQ/emnn9C/f3/ceeed8vaRI0dwww031EfbiKiuuPsCbYYBLoUTJBARETljIPvmm29iz5498Pf3l7d79OiBmBgbpskkojoV6OWG99ecsG2aWiIioiao2n1k1Wo1AgMDS61zLSyC7gjEhAjiImYiI3Jkz47tiNwCE04l5sDRmFPTkDxvnr2bQUREzpaR9fb2RmJiIlQqlby9cePG4uysIxCTISxduhQ6XeHgFyIH5apVo1WQJ/63/Rw+XHuyTo/tpnHD6nOr8fbOt4snS6hLYmKEghMnEPb++3V+bCIiqnuKosilwWyBQ2dk33nnHYwbNw7R0dEYOnQoTp06hVWrVtVP64ioUvcNaY2sfCNOJmbX6Zma1mUaugRaa7vqTXq8s8u2KWqrQxMYCA9OsEJE5FCy9EbHDmT79u2LDRs2yClpRXQ+aNAg+Pn51U/riKhSarUKAZ5u+ONQAv45lYLB7YLq5Iy5a90xJGKIvC6mqCUiImoSXQtSUlLg6+uLa665RmZmRRAr1hGRfVzbrTk83TS4Y+G/eOXXw3V+fI1Kg4z8DLy76906PzYREVGDBrKjR4+2aR0RNYxmvu5YNXMwXhnfGeuOJVW9g9lovdhIdDF4ut/TWHx0ce0aSkREZK9AVkx8kJeXB4vFIkf8i+viEh8fL5dEZD9i8GWXMN+qN/SLAk6tAd5rC5zdBOz7Dji4tMpjXx1xdZ211WvYMPhNmYKg+++rs2MSEZFzsrmP7Jw5czB79mz5j5qn56Wi6j4+PnjyySfrq31EVJc6jgMeOwD8Pgv434RL69sMBzzrpn9tVVxCQ9H8tdnyujHRhgwyERFRbTOyr7zyiszG3n///XJZdMnIyMDLL79s62GIyN78WwJXPVN6XWFZFSIiIkdS7aoFr732Gh599FHs378f+fn5xet37txZ120jokboRNoJXMi5gGGRw4rrSRMRETnEYK/p06cjPDwcCQkJeOmllxASEoIxY8bAUYhZvaZMmcKZvYhq6KF1D+GxjY9he/x2nkMiInKsQDY2NhbPPvss3N3dMX78ePz000+ypqyj4MxeRLVjtFgrHpgsJp5KIiJyrEDW1dVVLt3c3JCWlgatVou4uLj6aBsRERERUd31ke3QoYMMYO+44w4MHDhQTo7Qq1ev6h6GiOpJvtGM3w7Gy+Vt/aPk7F+OIP/4cRhiYuE9aiRU6mp/xyYiIidU7UB20aJFcvnYY4/J6WrT09PlLF9EZH/peQb0eX0tcg1mebtvS390bOZTJ8dWoCAhNwH15fxDD8F0MR4Rn38G76FD6+1xiIio6ahW2sNsNsv+sUWuvPJKXHfdddBoNPXRNqqSAo0xm+eJJK1GhTyDGWZFwfHXx8LHvdrfUyvk7eqNVr6tMP7n8cgyZNXPGTcW9rk1se8tERHVQyArAlaW2WpE9BlwyUvC75oR9m4JNQK9Iv3wf3f1xZL7BsLdpQZfLvd8Ayy/B8g4f9ldXq5eWHH9CuSb82FWrNleIiIie6t2RzRRqeCdd95BcnJy8TS1nKLWXhRYtO741mWK3VpAjYeo6Tqqcyh6RflXf+fsi8A/HwKHVwBnN9ZH84iIiOwfyD711FN4/vnnERoaCm9vb3h5ecklETmwr8YCFgvg36p6ux3+Ct2+7YZvj3xbb00jIiKqs0C25PS0os9s0ZKIHJgxD7j1eyCgeoHsucxz1mWWdUlERNSQWOOGiOwu8c23YM7IsHcziIjIwTCQJSK7S1+8GKEv/AdunTvZuylERORAnC6QXbZsGaZMmQK9Xm/vphDVK1etGs+uOISEzHyHONNeQ4dCBceYvIGIiBw0kE1JSbFpXWM1efJkLF26FDqdzt5NIapXi+8dgKSsfJxIZK1hIiJqmqodyI4ePdqmdURkX2JGLy+3upsUoSQfVx88sfEJ7EncU+fH1h88BFNaGrLWrmW/WSIiqptA1mQyyXqxokqB+Fm+qH5sfHw868gSNSXZicC+xUBOcoWbfDzsY3QN6orY7Ng6fWjv0aOQ/v33OD1iJC488iiSPv64To9PRERNi83pmjlz5mD27Nmy6Lqnp2fxeh8fH8yaNau+2kdE9SG4PdDxOsC7OWAxAvp0wL+l9b6Nb1iX/e8Hxr1X7u4eLh7QuVzqnpNZkAm9SY/E3ET4ufnBz92vRs0KeuAB6PcfQM6mTfK2YjDU6DhEROQcbA5kX3nlFXl58MEH8dlnn9Vvq4iozimKgiy9CV7uWmh0/sAt31W+g8VU6d2dAzrjYNJBDIkYgvWx63HjrzfiQs4FtPVri58n/Fy3jSciIipHtTvQiSBWdC9ISEiQ3Q2KREVFVfdQRNSAFu+IwUu/HsE9V7bCy+M7l7+Rxs26VGmqPN6jvR+VF+GH4z9gzr9zirOzREREjTKQ/fbbb/HII49Aq9VCo7H+Yye6GyQlJdVH+4iojlzIsJbhupCRV/FG180FLtwOxGwHTKVL1GlUGgxoNgBpBWkI9Qit9LHOZp6Vy9a+reui6URERHUTyL722mvYuXMnOnbsWN1diaix8wmzXi7svRTI5qUBLjqoXXT4csyXgCEPMFVcmzY9Px0Tf50IURJ24+SNNe4vS0REVOflt4KDgxnEEjmL8zuBd1sD31x3ad3C0cB7bYCL+8rdxaSYrBeLCQYLB2sREVEjCmRvuukmfPLJJ0hLSysuwSUuRNQ47YtNx9y/TiA5u6D6O2cniGFiQNbFEusuAooFyLFPdyJzRgaMSUmw5OXBEHfBLm0gIiIH7Vrw3HPPyeWjjz4q+8aKkdBiaTab66N9RFRLH6071WjPodrTEy5hYVCgQO3lJde5tWuLnK1b4RoRUe4+Z64bL4NZ1/BwGGJiEP7hXPhcc00Dt5yIiBwyIysqFhRdRPBatCQiqi6NlyfabliPdhs2QFMYyIbMmoVOhw7C59prxUws0O3bj4Kz0cX7mFNT5XoRxAqm1DSeeCIiJ1XtjGxsbPkz+bD8FlHj0yLQA6eTc9DMxx3xmRUP0GqsMlf9hmCLBRd37UKrFcvt3RwiInL0QLZPnz7FXQry8/Nl/9jAwECW3yJqhBbc2RcmiwXPrziEn/bVsj9p4lFRaw8NymKRC6VEzeqyDLExSP5kPnzGXQO31iz3RUTkTKodyCYnl55//aeffsL+/fvrsk1EVEc0ahU06qonN6hSbhLw+WBrICsGejUi6f9bJJfGCxcQ9tab9m4OERE15j6y5VUx2LhxY920hogaJzFdrWIuXDauQJaIiJxXtTOyJUttiUFe//77LxITE+Eoli1bJi96felZi4ioHAXZPC1ERNR0MrJeXl7w9vaWy4CAADld7bx58+AoJk+ejKVLl0Kn09m7KUQNzsNVg40nkjHn96Ol1j+waA+ufm8jzqXkXlp57Dfg14cAvxYN31AiIqL6yMiKcltE5Jhu6h2OMD8d/jpS+leUHdGpyMgzIjo1Fy2DPAGvUGu/2N5TgZ63A1+NrtkDGnIAj5C6aTwREVFtA9kiojuBqF4QEsJ/pIgchVqlQstAz6o3HDAD6HitNaCNP1DzByzgrH9ERNSIuhYcO3YM3bp1Q8eOHdGhQwd0794dx48fr5/WEZF9iOoEfpGA1hWoqOrB/u+Ab8cDcbsbunVEREQ1C2QfeughPP/880hPT5eX//znP3jwwQerexgichRhvYCbFwNTV5Vef/RXIHoLcHpdvTfBnJaG3J076/1xHEX2ho1ysghRz5uIyJlVu2uBCF5vu+224tu33HIL3nnnnbpuFxE1puxsp/GA2WiXhy+IjIRny5a48MSTaL/1H7nO//bbYU5Pg65vX+Ru2waYnafvvspgQNxjj8vr7p07wa1NG3s3iYjIcTKyGo0GR49eGvF84sQJqNW1LkdLRHay8XgS8o3mWh0j2CNYLtv4toGL2gWBptodrySLlyeCH38MisEAQ5x1drLA6fcgfO5cBNx2GyI/+QTu3bpWfSBDHpB4BLDUXdvsomQWloNvicjJVTsjO2fOHFx99dXo1auXHOwlZvVatMg6sw4ROZYCkxl3f7ML1/cIw77z6TU+zoioEdg4ZSO8Xb2RnxYN9acDMahlZJ2106VZM2iDgxF9/fWlA7nq+PMZYN8iYORsYLA1o0lERE4SyGZlZSEtLQ1jx46VGVkxEYLonxUaGioHfhGRY/B00+LIxUzMWnoAb95kzWS+PL4zbv+/f6t/MDdf4O+5gGcQgvrda13l6g1LYbB5IS8BF5KM6BncU37xrSltYCBafPsNTg25qsbHgD699JKIiByezX0CnnnmGezZs0deDw4OxnXXXYfx48fLrgXPPfdcfbaRiGqpRWHJLbG8ql0Q5tzQDdvPpNT+vA5/EehxC3B0JbB0KrC39K8z0/95Bnf9eRf+TahBkFxN+oMHcXLIEMS/9HK9PxYRETlYILtlyxZMnDjxsvV33nmnvI+IGq9HR7TFqTnX4J4rW8rMaJsQG2rJ2kKU5/IMBqI3A0d/AXZ9WXyXm8UCo8UIrVoLvVGP38/+jgm/TJDL+mA4cwbm5BTo9++vl+MTEZEDB7JikFdFavOTIRHVP/EZddGoG+yzKv6w/HAxEYuv/hht/drKdbsSduFs5lm5rEqqPhXH01ifmoiI6qiPrMlkkv1kfXx8Sq3PzMyE0WifsjxE1Hi1FX8XAjrVaN/PDnyGzd4b8djQNvDuNLTO20ZERE6Wkb311ltlNwJRR7aIuH733XfLWrJERFVx17jjz+g/8dXhryrdzqyYkRCgws47eyO/cDBpvrmg+P4H1j6AFH0d9PElIiLnCGRfeOEF+Pn5ITIyUpbeEhdx3dvbGy+99FL9tpKI6lyByYLzaXm276BxAW5fDtz4BRDSpUaPeUfnOzCx/UTsTrBxWtvMOLgaMuTVPLUJuW6A4uGOi+Y0XMix1pSttrSzrFxARORsXQtEH9lvv/0WL7/8Mvbu3SvX9e7dG204qwyRw4n094C/pyuunWedKctm7UZZl6mnAI0WCO8LZJy3eXcx8Ku5Z3Ocyzxn2w5nN6KzORbAzVB7e+HhhzTYcvN6GNZORo20GAzE7gDWvgJcP69mxyAiIsedEEEErgxeiRxbiI87vp7WD0Pe3VjzslviIpz6y7rUupe/bdZFYNVjQPshQOEMYNWhVozIMeRg9rbZyNdpoCnTT98m614FTq8Hxr0H5CQAKaertbsYfPbqtlfRyrcVnuz7ZPUfn4iI6gXnliWi2hn0CHD3n8C1c4HcFCBuZ+n7DTlAbhKQl1bjh4jNjsXOhJ34cvSXMqtbLPUMsGshYNRXfoDov4GrZgE9b6/R4+9O3I1NcZuw7OSy4nV6k14GuEREZD8MZImodkTf2RaDgLYjgMDWwLJp9XJG3TRu6NesX+mVa18Gfn8SOG/DhAuB7QB19f/kbbuwDU9tfgoRXhGl1s9YOwNDlw61qZwYERHVDwayRFQ3vJsBI169dNvVG/jmWsBsqp8zvP0za7cGF0+gcEpcwZiQgLRvv62zh0nWJ8sAet7w0n1qk/KS5JLVE4iI7IeBrAMzWxQYTRaoOSEFNUYPbQfyUgFLPQWyiYeB4S8B3acUr/Lo3x9+UyYj5dPPKt83/Zz1YiNV4X9ERNS4OF0gu2zZMkyZMgV6fRV96hxAvtECiwJ8eHNPezeFmqDTSTmY/s0uLN4RU7MD+EVaux0A+MvTA9tyY6CGdXaxI6lHSvU3rTH/lmLasuKbKhcX+F53XeX7tB4O5KUAv8+q1kOpVWrZL/bdXe/Colhq2mIiIqpDThfITp48GUuXLoVOp0NTIP4N79PC397NIAekVl8KAEtm9TccT8S0r3findXHsf54Epbttr28VkV+8/JEd11z3NrxVlzX+jqMiBqBpSeWol4U5FgHmP00A7CUE3BG9AEGP1HtTLGoWPDiwBex6Ogi5Jvy0Vik/7gUMXdNhf7QYXs3hYiowTldIEtEVuF+Oiy4sw8WTu2LAE/X4tPy+8EEbDqRjLVHE+vkVHkWBpPDvVrBLy8NOpUWQ8KH1N/LkHQUMBuBgz8AFhunzxbVFnKsfV4rIjLJo1pY6+g+tP4hJOQmINA9EHP+nWPXAV9Zf/6JvJ07kbttm93aQETkMHVkiajpGNOlWfF1kZRdsPlMnfcFnZuUggtaLbomrAC2/h8wZBbQ7lIgazAb5NJVcymYriklPxP44ylAFWT7TgXZwMc9rRnaxw8CXiFV7rIncQ/mj5iPHsE9MGvTLBxNPXp5RQUiIqp3zMgSkTTnxm5IyirA9rN1Wxs1wGJBN4MBKn1hHVkxyOr474AhD9mGbIxcNhIjlo1AliGrxo9xwpQpl2cyT1v7zbpXY9IEkwEwZAMmvbVLgo1a+LSAr5uvvAj5J0/i/EMPI+PnX6r/BIiIqEaYkSUiSfS1DvfX4WxKbv2ekcMrAA8dDMGhSM5LRnpBulydZ8yDj2sNZu0CcKy/D44rariE5WNQu7uBP1bYtF9ibiJ0Bj3Ke1SzxYyLuRdhtKF7QmZBJrK3bUbOhg2wZGfD78YbavAsiIiouhjIElGDCzeaEK+yYOrqqXVyPLMpCX/1UWMQvIDO4wFcCmTP/zIboYnHoRKBbol9jigFuHX5KLTwisCqkgfb/ZXMGH/Zuhc+OfWjzLh28O9Q4WO38WuDzw98jvaJV6BFnTwbIiKyFbsWEFG1nEzMxsbjSVAKJyHIM5iw+nA8krJsH8nf3mjE1wWeyCjIqHS7bLUa+bg02UGF0qOty+D2l93lsv9/+Cx/FNabe8EQdwEpCxbgzLkzSIAJChSkFqQj85wOGWc85HPS//ENElftwMXo3cXZ1oroDxzAlJ0umBY1SWaUBXNhaS6R7S07WYLoDxydGS2zvUREVHsMZImoWu5auBN3f7MLW09b+9J++Xc0Hli8F6+sPFKt4wTlpCLYAnS0lP/DUGsXH2z20GG+ywVg87vlblNgMiMt1zpYTHLxKHe7zeYesKi1SPtqIZI//Ahrv/sRL1usVRm8MnJwcYc/4nf5wXghAYmbMpF2wAeGbafR2rd1pc8h8Z13kTx3LiK3nJa1cQURqIqKBtf8dA3G/zy+VKmu93a9h+t/uR5fHf6q6hNERERVYtcCIrJ5JjnBYLZmHA1ma1bRYCq8bbLgfHo+Im08n6HZSdiQbb3erVWUrP9a4F6ArALroK+RHlFori3Ar6o0IOk44Hd5VYMlO89Dey4eqEaRgiJZRV/jK8iODvVqgTZtxuPjvR/bdLx8c4FcGi0GpOanyr614mJWLh2/KANd1C+YiIhqh4EsERVr5uMul0FebkjJsQZmQqbeiOHvb5K1VHMKKp5IQG+sxU/mC4ZgVq9rsDlxFwLcAzDJLUyuNqtEAFt+t4U8g7ncgVplqS0WqPTWn/5LcjMo0Nbxr/y5xlw8tO6huj0oERGVi4EsERV786ZueGZsR3y1NRqfbTpTvF4Er6klf8KvD6YCJGVfkFfT8tOAwkC2VrIuysXk3RvQ8tcvkR0aWnxXVJKCt782I8uzxPaiP2wtp59t598eafmXzh0REdUf9pElomIuGjWCvd3qeEoEGwV3staYrYYdLrvxXttT+N2zZDQqqIDgjsCnA+GPTPjlWevDmhIvzVbmn6NAawECCrs3SD89KAPq2ig6d2qVGt4u3rKfbHo+uxIQEdUHBrJE1DjcMB9w0ZV/nwguxcxbmResWdbCfq3p6gxYVMA515KFtQo9/K+cqtYNNk5TK4iJEYIur3xQE+4ad/x2029yooeqqjMQEVHNsGsBETUqYopcURZLW/g9O1VlwBx3I7T5BplhFVu4BHviLVGeS6W5bP9cYx5yTbl44Z8X8DqA//r7orVr6UAyPFVBRMrleefsC+7FZcUErcr6J1KrrtmfStHXV2Rml/08B67R8bjQq/yqCuUxxMUhZ/Nm+IweDW1wcJXbm9PSkPbdd/AaMgSuUVE1ai8RkaNhIEtEjcqzza6Gf7trMPTcHvyRsg8XLamw+DXHpwNeBRaOk9t8nJiHx0ODkKopnPa2hKS8JBjNJqw8sxKvAPjNyxMzNQac798NnbyCsa+tCtpf12Pq+tJ9YQPa5yD5kA8U89nCNQpuCemPkCvnoEezPrV6Tn0WbEVYOnBcrwa62PZDWPLH85C1ahWMsecR+vxzVW6f9u23cpk3diwiPvqwVu0lInIU7FpARA1H/Gyvca3453utO5odWI5x+gJ4qC91FxBT13YM6QH4tQB8I9FB5QY/S/UGZcX36ojIT+cjYWR3/Nn38j99RwcaUaArMflC6mm4f3oFxp3ejnCvcNSGuvCwqsKlKDEmaskeSamk9m7h81OqO/ismueFiMiRMSNLRA2ny43AsP8Ax/8Afrj1srtzPcKR5OUBfUYidIoFV7r4o7fSGzd0ugFw8wIeP2jd8INO4sd0ebVTthdaqhKh8W+Fy/OztnsuOBDvaiwIKVphLuxbm1t6dq7a6BnSE+oIP/x65ld5u09oH3wz9ps6Oz4RkbNhIEtEdcbk5ocCRYsMeEGbU4DAojs8g4HcZMC7WYX7Gi0mxGfmIiErH6Z/5gH6C2h+1dO4zdgPY9qNqXC/cL0O7+am4qfgnlgjJ2oogJsRuHWTGRn5HlDZmEytaiJcxWxGzrff4dbtZmR5qKBMqDjzqbIoGLu9AOmaH0VKtXj9LR1vQabPsUvHLHFfRfR79iL3353I+3cHFKMJnpm1GziWf+IEsn77Hb433gC31pXPXEZE1NgxkCWiOmPwaYmhygJ46XS4+O5GHPIq7L807n0gaiDgdamOa1l3//M0jOo0+Jq84W26AAx4EBj6PLB2XbnbhxtNOOzmBl/TpT9jx9OOY3rcv7hjsDc8snOQus8b/v1sa7tJVXnRMcPZs8ia9xk8ugMTdligxMUXl9rydfMt3s5F7YKoDC1uWVeAhHWvwv0ZL9SU74TrYYg9j9ipU6EJDoLngIEI+O031EbaV18j89dfYcnNQbOXX67VsYiI7I19ZImoRt5fcxJnkq31WYU9selY+E80zG5+WPPsOOQazMg0WEO9NJObNRtbFCxqC6ebLVENIFGfgtC8xxFVULhOdCVQX16VoMjbyakYHnMT2uZeChTFRAoqVQhSx3+IhWM0lzqlAtgfm4HjCdbpb2tK5e4mj2sq85ezT2hvzOw5U17XaXX4ftz3qAuubdvCe/Ro6/XIKATPfLhOjktE1FQwI0tE1TZ3Sg8s2HwWO6OtvVIn9o6Ar85FzggW4u1WvN2k3KcRrkrBndpe6J9nxI7oVLm+V8SVCLljBSAGdP3v+uLtVYrtf5LEloNahCAywwsoMd9AboEFK/cnAJGltxcx9PYzqYB3fbzgKrhqLg1Oc9NeOgdERFR/mJElossMahOEbuG+uLp9+fVL24d6o5mve/FtV60aA1oHXLbdGSUcWyw9ZBT5wdoTePGXw5i98gheWnkEaDsSaR4ta3X22zfzgkZdeZeAsGTAJw/wcqs8SJ7Z/QEEuPkX39YWqJB5Tgfj6UPIWvwJCs6ckTVun+n3DLRqDZp5VNzf195E39vsDRuhP2gdHKc/cgRZa9fa1CeXiMiRMCNLRJcZ3C4Ig9sNxurD8fhs05k6OUMGkwW39o+SGdv1x6xTxZottgdW+UYzpn29U1aX+kJvgF/hlLrbA66FTm0BonoAJ06V3kmlxr2/KDgbClwIC0WrMsfM8NUiIjIYGm9P3N3nQZzR/gxjYXq3+T5XXFTcoNqVCcU8H1CrofbwwJ2d78Rx1fvQqBtvHiD/4EHEPfQQVDodOu7bi/P33S8nTIj65mt7N42IqE4xkHVQMZkxOGdKhJelnKk5iRyMj6gIoHGBGpeyvGVl5Rux46y1K4PezQI/FdAlzAdLslvjJ+8uMOfuuHwnlRpqixlLxnjhiM9SHDj7MxQYMVwjfvrXo8BNjcjlG8p9PJVizfQq5grqs+rrZtrZzIJM3Pr7rbJv7ZJrl8BV1NmtJcVkKn9ptC6JiJoKBrIOKlmfDC+1Ox5L5UtIjktx8USS4oev4nKRd9cveGnVpcFjlYm2NEOoSzbUftapWL/dfg5an/NwDyt/+xtbTYPaEIbu4X64oVc4zmV9BcBay7VGzAZg4WjAeCVqSwxQO599Xl4vMBfUSSBLROQsGu9vY1QlDTTwKMwaETU00S/2v+tP4Ydd5+GmrdmfEsXNGwMKPsHYvE/QzL+jzfvdbvwP9t91HAjrZT2OIn/5l1TllNFy03jAUxWJYLeWaO/fHtoS1RJq1nBFpDkBMzOcRET2xHQeEdXInBu6Yv9568/rA1oHYsdZa0WC6lIq+D5tsVy65+jFLFhKDFSS95QpzdUzwg9HTUCYrzuSkqzrVO7uUHJzobi6APnlPTaQkJlfauCa3E+rgmJSoNa5wKI3Qu2qwGJQQaWruOtDuVxc8NT3efAosN7M+HEphl48heNXWHAxQIV7vjkEQ++5mH3ABG89kPDDFKToPBDx6fxyD6dyLczWarVAYXcBud7FBYrRKJeVSV24EPmHD1e5XeJ77yF3yxaEvfsu3DuJWdSIiBonBrJEZLP+rQKQlmuQgV+QlxtGd6n7kfurA+7EqfSW8LEMx+0ATmYomDHvb3i4aorbkJJTgHA/Xan9VIXVC1xKZIcjfvgOU5dMxeRWUcCByx/LaLZg4mfbsPW54aXWh46NhEv2PujG3QO993C4n18Cw4kD0N6/yObnIQaGtViyBMabJsClsJ9t3u7dsvpXp1gVXI1ASLwelt/Xoa0GeGeSGh8NexIZr8yBITq63GO6hIUh/qlZGDRkCGJuv6N4fcjTT8G1dRvkHz6E/KOXZg4rK2/HDgTcfbfs76sYDRVul7vlbxScOo38I0cYyBJRo8ZAlogqFZeuxwd/nZB1WJfOuKLez9ao0ddi04n+CII1+5hvsmZi8wzWaPD7ewdAq7GtK0NMgAmnIirfJjvfeNk6jU4LL68CQOcCr+GjgTNaaLMPAy2rVy7MvUN7WERTiwaMVcCiAg62VsN90ECodaUD9LKMkZFw79Ch1Dq1tw+8Bl8Jw5nTVbbJrW1bGM5FVxrIEhE5CgayRFShIe2CcfvAFrJ01rsTuzvcmbrj9zugMochVBeOUrMmNCCNSlN1JEtERDXCQJaIKuTppsWTo9rb/QyJwWQGswUeLppyB3OVpFapkFNghgcAk2KC/vytaO4dbLdA1kXjCgV6uzw2EVFTx0CWiBpcboG53J/0SxKDu04nZcNgAVoFeeK/t/aCu4umypm8XDQqfHlXHzy6zXr7g8k90bGZT901PvUMNG4WxKwPAjwKR5Upl2rMGs6fR8y0aVD7+crbGl9fmPR62WfWkpdX6aHPZJyp1Qy6al9fZG/YAEOctZxXRUSb0r75FtrgYAQ9+GAtHpGIyL5YfouIGlS7EC8cvpiJBxfvrXS7RdtjMHLuFiw+YQ0S24V6IzJA5FnL56NugSjvKAxsPhAdQoJhymsJc14UOoXW4YC0ghzgs0FodXuQSP3ClJZlXX9hT/EmpoQEWRWg1dKl8nar5cvQZvWfcOt8afS/l6sXWni3KHXo/s36Y9rqaTBZal7Sy3fCBIQ8+SQKKhnwJYgBXwFT75JT1xIROTIGskTUoESpro9v6SWrH5T098kUWZ61SFa+NaDT2xjX+ahb4febfsez/Z+Fq8YN+pgHkBfzEHTaS8GveMzle+KQV1C6z+o/p1Kw8URhdrUyFiNgyof20bVQlRxwZiqsr1VI7eIKbVCQvC6WrmUGid3U9iZcEVZ64NzCMQvhpnGDIouC1YzoduHSvOrAXaXRQBsSWuPHISJqLNi1gIjs7rYBUXjhl0MwmmsexNnivxuso/qvSb6IRwv704qA+Y6F/8r1qy2VPH5BNnD8j3ptHxEROVlGNj09HdOnT0dkZKS9m0JEFdgXm44DcZkVnp83JnSFv0fDTc1aFK9W1d+2WEhnILAtsPsroOtEQFvNiRGIiKjpZGQfffRRrFy5EjExMTh06BC6du1afN+pU6cwdepUpKSkwM/PD9988w06d+5c4bH8/f2xcOFCjBw5soFaT0TV9emmM3Iigxt7iTJY9nH3la0Q7O2GnHwT9H9Z14kwduawtvDRafHu6hMV7+zdDJj2W4V3Z+87C1dTI/ypXlGQtmgxFL3tVRMyf/0VpqKp0Uow5+QgdcECuLXvAGNCPFRqDQLungZV0dzARETOEshOmjQJzzzzDAYPHnzZfTNmzMD999+PadOmYfny5TLbun37diQkJOCWW24ptW3Pnj3x0UcfNWDLiaimJvQMk9UHiuSbzPh4/akGO6EDWwfKyycbTuFQifVPjbFOLvDemkoC2UoEtM9Bbr4RmSt+gueQIXISBPcePdAomExInDMHgQ/MQPbqNTCcO1flLolvvgXv0aNRcOZMqfW527Yh9f++LLXO9/rxsvIBEZFTBbJXXXVVueuTkpKwd+9e/PWXNV0yceJEzJw5E+fOnUPLli2xadOmBm4pEdWXs8m5SMjMx8e39ITa1p/4G0poZ0CVCrS8/Mt2We6BBiieYSiITYHGxwchTz2FxiZg6lRkr1tn8/b+t92KpPc/qNc2ERE1ucFe58+fR1hYGLRabfEI3KioKMTGxspAtiIPPPAAjh8/LpdPP/002rRpc9k2c+fOlZciGRkZWLNmDRpKfn5+nT6eCPo9zCaYzeoGfR6N+Zw0BY58TvYnW8tkFRQUYM1flz+Ho0eOYk3GcXn9QIp1Wy3McE88jDVrDqOgwFqeYP++fYhLKz3oKic7u8Lzcv68tQLBhQsXsGZNgryeZbi0/5YtWxCkuxQonzpngSXf2sXBU+VZfFyLRZHn3020LycQeb1mA9EAost/3NayxIKquK++EB8fj8MVtDMkPR1FPWvPxZyDYm5eavYv0Q6z2Yw8vR67du2GV3w8DCLA12jgX/gYJ9asKX6PRJhM2Lp1K/xTU3H+8CHkelintvU4cADWegmXbNywASE5uXLS38OHDyPXyxNex4/BPTEJR8u0N9xoxLZt2+CXmoLYw0eQ622tbKs7cABlc68bN22CxacOa/Q64eemvvCc8JzU9fvEpXCZl5fXqD5vjSqQFcrO2qOUrMdTgc8//7zKbZ588kl5KRIREYExY8agoYgXvS4fb82+VdBHn4NGo27Q59GYz0lT4NDn5EgCcHgP3NzcMGb0CGCTdYT/i9d2krNy3TmwBbzdrX8K1UcTgUO74erqhjFjrP3bX9q9DjAUoGevXsg4kQxcjC0+tJe3N8aMKf+XnC15h+S24eHhGDPG+pN+Sk4BsHVd8S9AJevP9s0pwOaUGGAvEOAdgH6F51u95Q+4u7vL4lc9evSATxWvw+kXVaX66qfjPJo3b46+Fex3btGi4vm9WrZoCfeuXXFx8Q/ytovGRb7uL3/3Mjx0OnTo1xcZMTFwa98eKq0LkvCrfIxeY8YUv0dOvPAirrzySiRs2oxWXbvBr/BxsywWXCjz2MOGD0fM119DFDwTYxLEtmmJScjNykKfMu09+fIrGDRoEJK2bYdP1y6XjqtSXX7coUMbRdcCh/7c1BOeE56Tun6fbHjduvTw8GhUn7dGFciKygNxcXEwmUwyKyuCWJGlFVlZInJMYpBX9wg/NBaBXm4Y3jEE8fZuCBER1VqjGm4aEhKCXr16YfHixfL2ihUrZJeCyroVEJHjGdw2CI+PbIfXJnSBM8rZ+g/Sf7RmY0vyd/dHfG484j7/BPoDB2yqMFBwxlobt6bEtLkJb8xB6sKFtToOEZHTZGQffvhh/Prrr7ISgSib5eXlhdOnrX+MFyxYICsWvPnmm/Dx8cG3335rjyYSUT3SuWrw+Mj2djvH3mPGwJScBF3PnqXWuz79PHwunIPX0KHVOl7A6B7QdLoKPqNHV7mt/223QRMYIK+HPPYY9IcOwyXC2mf3x+t+xEunr8fdShQiBlwJv4kTkfnrygqPlfrlQvhOvAneo0YVrxNtD37ySbh36gjjhQtQubhC41dxRjz/+Amki+SBRoPA6dOr9byJiJwykJ0/f768lKdDhw6y3FZ9WbZsmbzoq1FXkYgci5/OBfcNaSVnCgv1uXzyAo2XJ4IeeODy9QMHISjwUlBoK9cgHwRf/7BN27qEhyNw+j3Ftz369Su+7uvmi+j2PigYOAHBzQfadDyfcePk8ymi1ukQdP991Wo/EZGjalR9ZBvC5MmT5UUM9iKipkmrUeOFayueSKWxM1lMyMjPgJ974+lbTETUGDWqPrJE5LjahXihTbAnBrcrW/yp+vq28EdkgE5WOgjRAVe2rfiYfVtat+3XUhSpahpe3voyhvw4BL+f/d3eTSEiatScLiNLRPWjdbAX1s8aWlyTtTYm9omQF2Gw7gLGjKk4u3pjrwh5aUqS9clymZAr6uJq7N0cIqJGixlZIqIa/fUsDDDd7D8hABGRs2Ig24gpBgPOjBmLk4OHwJSaau/mEFEJmqhOcqlt2dWm86INCLQug6xLW2kLKxxoA0vvJysfqNXQ+te8S0X+0aOIveceaMQxzGacGXetLMelLpzNq0jO1q248OSsSqsfEBHZA7sWNGKK0QhDTIy8bk5Lu+wfMiKyn6ivv4ElKxMu/zxv0/Zh77wN0zNPy6oFlfFy8ZJLndY65azP+PHQ9e592ee/1bJlsOj1cAkNrfFzMF2Mh1vHjrJt0RNugOHsWbRetRKuZQbDGqLPwaNfXwTdfz9i775UcYGIyN6cLpBl+S0iqgui5FXJsldVEWWxygaI5flkxCdIzEvEN4e/KZ62u7z9ND4+8lJbKjdXqD29Lh03wJoBLkut85DPgYioMXG6QJblt4ioMQvUBcoLVPZuCRFR48c+skTUKDT300GtQrkTGBAREZXH6TKyRNQ4/Xj/QBSYLPDVudi7KURE5CCYkSWiRsHdRWP3IHbt0cRa18AlIqKGw0DWQeTt2Yu0b7+FJT/f3k0hqpJKBdw3pBVu7BUuJ0pwBPcOaY331pzAvvPp9m4KERHZiF0LHETCq6/Kpajj6Dthgr2bQ1QpMdL+hWsrno2rMXp2bEes3H8RTTUhG3DnXcjbuROeg66Qt72uvgr6/fvl0laubdvBb/IkaAICkbpgQT22lojINgxkHYyiNNF/ZYkcgNmi4O0/jyHfaMGL13WCm9Zxpo/1v3mKvBRxbdEC4XM/kNcNcRdsOoYoN9b89dehmEwMZImoUVA7Yx3ZKVOmQK/X27spRORgcvJN+L+/o7FoRwwuZrCbDxGRvamdsY7s0qVLoWNhbyIiIiKH5nSBLBFRVVbsicNNn27FP6dSeLKIiBoxBrJERGWsO5aIvbEZ+PtUMs8NEVEjxkCWiIiIiBwSqxYQERXSqFV4+8/jMJktje6cpOhT4KH1sHcziIgaFWZkHYlajdTPF0B/6JC9W0LUJM2/rTeSswtwIC6zejumnQXWvwYk1M9nc1fCLgxbOgwz1s5AQ8vduVNOxqLSVpz3SF+6FOduvwN5e/c1aNuIiBjIOpBmr74Cjb8/crfvsHdTiJqkbhG+CPJyrf6OuxcCf38A/PNRvWVjhWR9w/fZzd26DS4R4Qj9z/MVbpO9dh30e/Ygd9u2Bm0bEZHTBbKOXEfWNSICLhER9m4GkdNIzinA3LUnsSfGuaetFZMnuDRrZu9mEBFdRuuMdWTFJYIBIRFV4ae91hmvdkWnYcn9A3m+iIgaGafLyBIRERFR08BA1oGplMY3spqoKTKaLcg3mm3bOP0ckJ8FZMUDOSX6tBpygdQzgKLY/LhmxYzT6aeRb+J0uERE5WEg66gyL0BrzEaMtqW9W0LUJEX46+RyYOsA7I1Nx73f7q56p9PrgI97AJ9dCXzcHfhvH8BYGIT+PAP4b2/gwBKb2/DN4W9w48ob8d7u92r8PIiImjKn6yPbZFgMsGjcsMDnEYyxd1uImqBpg1rizitawFWjxqaTyXjjt6NV75SfYV1mxlqXZgNgMQJwB3JTretybZ/2NtuYbV0arEsiIiqNgSwRUTlUKhXctBrrH0q1iueIiKgRYtcCIiIiInJIzMg6IFNCPHxOpMP2HyiJyFZ3DGyB5n46DO8Y4vQnrSA6GvkHDsK1bZtyz0XO5s0wZ2ZBMRjk7bxdu2CIiZF1Z4mIGgIDWQeU/v0SuIXocGiom72bQtTk3NQ7Ql7qjKhi0KwbHFHawq/g1rEj/G64odz74198qfi674TrkX/sOFIXfoXmr81uwFYSkTNj1wIH8svpX2CSA0eArPb+ON/Sxd5NIqKKaFyB8D7AF8OAzDiHPU/eo0dB17NnldvpeveB14jhDdImIiKnDWQdeYra38/+joTcRHs3g8gpmS0K/u/vs9ULZO/bAGhcAGP1/97c1vE23NzhZkxoM6Ha+xIROQunC2TF9LRLly6FTmetEUlEZItzqXlYsOUMXh3fWZbkqm89Q3rixYEvop1/u3p/LCIiR+V0gSwRUU25azWYdmUrgNW4iIgaBQayREREROSQGMgSERERkUNiIEtEREREDomBLBERERE5JAayDiTMKxwHkvfL63v18QiAdR54IiIiImfEQNaBiFI8oR7N5PVWrv6YYQq1d5OIiIiI7IaBrANxUWuhUVuzsC4qNbSsAUREREROTGvvBtDljBcvIubuu6ENDOLpIaJ6p3Z3A1xcoFKpoHKp3tTXKg8PKHl50Hh7wZLjjbSvv4FLWHMEPfBAvbWXiKgIA9lGyBAXB2NMrLwQEdU3bVAQ2vy2ClCrofHxqda+EfPmQePvB/fOnQGzGeb0NOj3H6i3thIROXXXgmXLlmHKlCnQ66s/9zkRUY2YDY3+xLm2aAHXyMhq7yeCWF2XLtZsrlYLl/DwemkfEVF5nC6QnTx5MpYuXQqdTmfvphBRU+AbYV1G9LMuvawDMov5hAP/NxxIOdnwbSMiauLYtYCIqAp+OleoVECAl+vld456DRgyC9D5A/p04MJe4LuJl+5/4G9gfn8gg12FiIjqGgPZRsaiWHAw+SD87N0QIirWLcIX/zw7HJ6u5dRuFpVEPAKs18VSU+bPqosOUDVMzWejxYh/4v5Bx4COaO7VvEEek4jInpyua0FjtyF2Az7c+5G9m0FEZYT76eDnUU5GthH56eRPeHTjo3hh6wv2bgoRUYNgINvIGBxgUAgRNU4Gi/XvB/+OEJGzYCBLRERERA6JgayDsigK4tLz7N0MIqf1xZYz0BvM9m4GEZFTYyDroPKNFuiNZjw9pqO9m0LkdN6d2B2/HYzHgbgMezeFiMipMZB1JNF/A4ZceVVjyYe3mwuuaBNo71YROZ0beoXDz6N6U7kSEVHdY/ktR3JwGaC3BrIupjwc8ByE3vZuExGVFtIZaD0MCGrHM0NEVM+YkXUk/aYDWnd51aD1xk6f0fZuERGV5RUC3PULMO49nhsionrGQJaIiIiIHBIDWSIiB2W2mG2uGVudbYmIHAUD2Uak4MwZhLzxDW7ZXHFJH53K+pLp0DBTXhKRfYV6hspliC7ksvvELF59F/fFroRdVR7n8Y2Py213xu9EQ1EMBsTPno2E115Dymef4cKTs2BKTa3x8UxpafIYKZ8vqNN2EtHlLOLz+8qrSHjtdSgmExorDvZqRHI2b4E2IRXbO6mxvicw8zfLZdt0UHsiG3loqXjapY1E1LDGthyLgc0G4nj6cby67dVS953PPg8FCi7mXKzyOEXbXsi5gIYiAs+MJT+UWudz3bXwHj68RsfTHzyIrD/+gGb7dgQ9MKOOWklE5TElJSPjxx/l9aCHHkRj5XSB7LJly+RFr9ejMTKGBWN13yQ0S1PKvV9VZklETZ+fux+0Kqf7c01EVCWn61owefJkLF26FDqdzt5NISIiIqJacLpAloiIiIiaBgayREQ1ZFHK7wJUOQXQpwP/3959wEdR5v8D/2x6QiihRQIh9BLpIKBIFYU76+sAFUE9T+48keM4lFN/9vt5tp+iJ5bjLx5NURPgVESlE5qQSJOEBEJJJb1tsiXb5v96ns0u6Sak7ex+3q/XvnZ2Znbmme8+O/PdZ5+ZsdV9UicRETUME1k1CQpp6xIQUYXQ9gF46NNYnEwralxMjv4beLMPsLvqiVtERNR4TGTVpFPvti4BEVXY+OgERIZ1QHpRI08cLa24wkBJBmNJRNRETGSJiK5BoJ83/Ly5CyUiakvcCxMRERGRKjGRJSIiIiJVYiJLRERERKrERJaIiIiI6hSXHQdXvWAgE1mVsVht8tlU8UxEbet8dik+2JuMhCsl+HDfBZxKL+ZHQkRu5dWj/4T/tVw2uxXw5t0qozVa5IdWqDe3dVGICMAH+y7IOLy987x8PnA+D189diNjQ0RuQ4GCTi7afsYWWSIiIiJSJSayRERERKRKTGSJiIiISJWYyBIRERGRKvFkLyKiljblKSA9Fii8BKQcZLyJiJoJW2SJiFra6IXAXe8D7box1kREzYiJLBERERGpEhNZFXn52wSYeSMEIpfh52PfhQb5ectnby9NG5eIiMizsI+sizLbj4uwaLxg8fJGgNUMreKFcigIFBcn5gGTqM29NXcE4jO1GN+3M2IvFyCjyIBdZ3PgDjQBAfLZyz+glokaaPz8oJhM8PL3rzHZlJ6Oy7+bA59uDe9KYTMakbH4CVjycuEV3B69PvwAPp07N6rM2h9/RP6HH6LLokXoePfdjXovkbuz5OUhY8lf4NszDD1Xrmzw+zS+vlixtgQ+JYDF2/Vu78VE1kUVdNTgpQe9kJ/+V5R7++I/d/WDNtUfueiM4eGpiO0c2dZFJPJ4vUKC5EOYPawHtp7IcJuY9HjlZRjvuxdBEyfWmKbx9kaf6GjYdDr4DxhQY7rp4sVGr89aooXuyBH0+vgjXHnmWZgzMhqdyOqOHUN58gXofjrKRJaoGlNaGgynT8OQkICeaCAvL/TZvBn/inoQD+TroEt0vbuKelwiGx0dLR8GgwGu7lKYBvml9uqmGTUGSE2Azc8bwWHlsFkqmmyJiFqAT9euCJ4ypc7pAYMHtUjcxTq9/PxaZNlE1Hjiux4/0A8mHwOUJLgcj+sjO2/ePERFRSEwUPxBT0RERERq5XGJLBERERG5B4/rWkBEpFYmqwmXTZdxIueEfG20GOVzqjZVjiu3lleZ/2LxRXhpvNC3Y1/nuLTSNFhsFvh4NW33n65Nh96ix+DOg5u0HCJqHqnaVLmPGBgy0KNCykTWBYW1C4OPTYOI/Iv4vq0LQ0QuoVf7Xujg1wGfF3+OrQe3ynEBPgG4u//d+O7Sd9iUtAlWm9U5f64+F3O+nSMT2Zj7YuS40d1H4/PEz9E1sCsWDF1wzWUR65mzbQ4MFgO+vefbKokyEbU+k9WEud/OlT9mt/9uO8Lbh3vMx8BE1gVd1+46vDrwKXTbOJ2JLBE59wtf3/M1duzYgVmzZtWIysqfV2Jtwlrna3FAsypW+RAtsMJjIx7D95e/h86sa1JUFSgyiRUcz0TUdqyKFUarsco/NZ6CfWSJiIiISJXYIktE1IzETRH2JeXiUr4Owf7euHdcODQa+x2/SgxmdGS0iYiaDRNZIqJmMnVQN/wQn41H1sU5x90+IgzB/vZd7YW8MoxltImImg27Frgov/yEti4CETVSl2B//P6mPtcWt5yzQKb9agRVWC3AxX1AqXvc+pbIZZRmA5f2A5VOkqQ62Kzwh2v2vWUi64pMZei+8wnstrHthsidnA8cjQu2MKQFj6g6waQH/t9U4JPpQFFK1Wkn1gMb7wH++1irlpXI7W1ZBGy4Gzi5sa1L4vrKtTDDDzYXTBtdr0Qkf/lYgntiiXkpo0HkRg53uhMzTW/jXKdqt35VrIDVZB+2VL0WLMyGqs9E1Dz43WqUtQEPwaq4XtroeiUiIiIiImoAJrJEREREpEpMZImIiIhIlZjIEhEREZEqMZElIiIiIlViIktEREREqsREloiIiIhUiYksEREREamS/QbgRETULDoE+Mrn9gE+KDVa5PBfvzyJwxfyEezPXS4RUXPiXpWIqBkN79UR+56aBkVRMOOdGDnuVHox8stM8kFERM2HiSwRUTPr27UdtEYz40pE1MLYR5aIiIiIVImJLBERERGpEhNZIiIiIlIl9pElInIzChQcyTxS5/TEgkR8feFrTLhuAnoE92jVsnmyU7mnkKPPwW0Rt0Gj0bR1cYiq0Jq02Ju2Vw6P6DoCqZlxCIWCuOw4lMN1MZElInIDU3pNwdnCs2jv2x570vYgqSAJcwbOwZbkLVXmu6X3LdiUtAlrzqzBgYwDWDltZZuV2dM8vvtxlJnLsG72OowNHdvWxSGqYn3CevkDt4NfB6RoUxBSaMYqRcGinYswxWRFua0jXBG7FhARuYFx143DmtvWYFbfWbJFtmtgVzwz/pka883oPUPON3/IfFht1jYpq6eyKvZ4M+7kiqw2K6aHT8fiUYthsdmvgS3YFBte0pbDR/GHK2IiS0RERESqxESWiIiIiFSJiSwRERERqRJP9iIiagtWE3B8PWAzA5ZK5wRnngBSDl59rcu1Pys24PSX6FySCWDW1XmzfwFGzgd8mrn/Wt55IOUAMGwuENipeZdNLc7bagTiPgV6TwRCr3efiBuKgfjNQN+pQNeBbV0a15V3zr4fGX4vENAB7oyJLBFRW0j6Dkg5ZE80hBv+CJz8DPj6z0DPcUC7rkBuIlCcap+eESsfY7z8ADxpH/f140BeEuATAIy8v3nLt+NZ4MJuwFAETFnRvMumFtczdy8Quw4InwA8utN9Ih77CbDvVWDArcDCzW1dGtf1w9+BS/sBoxaYvLzpyxP7gSC4JCayRERtpXsk8MBXV1+f/sL+fMsLQL9pwI7ngJ8+qPoeRak0bKv63Jycy660PlINDZSWqxttqSXrvDtRPCdO7CNLRERERKqk+hbZffv24bPPPoPRaERISAg++KBa6wURERERVfHOznM4eMaKsZPK0TXYNa8R67ItskuXLkWfPn3kLfri4+OrTEtOTsZNN92EQYMGYfz48Th79my9y5o+fTo+/fRTfP7550hNTUVpaSnURvvDD0h98CHojtR9S0kiIlXy8gK8veWgxtcXef96H/mffCL3eY5H7nvvIev5553zwccHOa+9DnNWVq2LVMxmZD61ApnLl8NW3ribZxZu2IDUh38P47lzMJw5I9ef9+GHSPvjn5Dz5lvV5t2I1IceRsGnn8r5tD/uuFoGiwVXnn4aGX9dBpvB0OD1zzlkQ+Cy1+VyDQkJ9c6b//HHSH3kEZhSK/pJVyLWKdYtyiDi0VBiG8W2mnMrTiJsJTaTCZnLn0Tmk0/JYYfSvftkbEv32m+N2hDlWh+kfp6KgjVr6p3PcPq0XHZRVBQ8LQcoOlmC1D1dYEjJqznRZsOV555D17dexPksA+IzS6pM9s4txHNfWHHfAXV0S2iTRHbu3Lk4dOgQIiIiakx77LHH8Kc//Qnnz5/H3//+dzz66KNyfHZ2NqZNm1blsWzZMuf7vvvuO0RGRqJ9+/ZQm9Lde6CPi4Pu0KG2LgoRUbPwjxyKXh99iIjPNiJiw3o53Pfr/8ppee+shFeHDug0dw4CIoei4N+rYThxQs6n8fZG+IcfwJKXB2MdiZ6trAza776D9vsfYM3Pb1S5SrZvh/7YMeiPxUJ3+Ijc9+av+gC6gwdRvKXq7Xy1338PfWwscv/vbTlf6Z49V8ug06Hkm29RumMHLDk5DV7/lHgbrNf3h63cCP3Ro/WX9Ztvof/pKAynTtWYZsnNlesW84iyNFTx1q1yW8vPnUNrshYWynhqt2+HtajIOb5s3z57bBuRyOrz/KBP0aPk2231zicSQ7nsSj9AXFHprt2ynGUxMc22TG1iGfR5/tCdFVc5qcZmQ8mWrRif8Qu66wtrTPY7l4aRKQqmJKijf3ybdC2YMmVKreNzc3Nx4sQJ7NxpP8Nyzpw5WLJkCVJSUmQL7v79+2t935o1a5CRkYE333yzznWuXLlSPhyKi4uxY0frVW7R9aGu9XXJykK7iuGSkmKUllnk/JUdPnwYhYVVfx3l5OS06ja0Zkw8FWPiPjExWOwHgT27d0Ovr3or2KJC+4G8sKgIcZW27RaLRe6U437+GYXJ5RiUkoK+tSzbEY9JOh2CAZw5cwZXcuzjThtO29dvMGD37t3OLlhlujIcP34cZfFlclyiLhG5ptw6Yzs2vwBdxb9kF5Jxybij1lutCj/99BPSfdOdr4PPnkVncXDWliJZtBTmVWoRunAB3by9ESiuHObjjbMBAQj080M3AGaNBgdFUlpRnlAvL1w5eRIG69V1BSQkoHu1csYcOABjUJDcjpC0dIimjMwrmThTx3aFFpdA/ImamJQIr3ITKl9YzGI2V4lHaHGxnNch68oVxFdM1+j1CK8Yf/DgIVgakBharPbbfiYE+2Kwlzeyzp2XyWhdeuh18AXwy5kz0AcEVJnmk5eHsIrhvXv3wtbOcRSxC6topRXHumOV1tHTbIZo9xZ1wajXo7V4FxejZ8VwzP79sHayR75zRoasw5kZdX9mlfVPv4AuFcPiH9j69g0dki/Izze/oACJO3bU2JdMKCmR0xOTkpBW0nb7mC7Z9hwgJSUVp65xX2dSTFXyhZsrPn+x7ccrlul/8RJCa3mvqAvlKV64XHoZBpsBl1N8nTGuTK+3/2DS6/UutU92qT6y6enpCAsLg4+PvVii60Hv3r2RlpYmE9nabNmyBa+88gpuv/12/PnPf8b//u//ols3sVusavny5fLh0KtXL8yaVXEtxlYgPvS61pe5cxe0FcMdO3ZC+2AjAsROyzFSHLAmTcLO/ASg0g/v0NBQzJo1FmpVX0w8FWPiPjHRGs3AwZ24ZeZMrDx7EDBcTRpCOocAeqBzSEjVbTvuA5iAG8aNq7hqwQGgln/Xne9JbgcYgOHDh2P4KPs4JUXBZzGfITAwEDNnzsSznz8ru2Bt+HEDxo4di0k9J8n58hPzUZpVilkz6oht7r+BEmDggIEYOLXqPOI+7E9vfFoO33jjjYjsEumcVlRYiGyRRHTogJG1fG5pokVQJCT9+6PbrFko9fVDhmgF8vevEovL/+8T9B89Gu1nznSOK/X3l/NWNnXKFOyNj5fvzTp6FMWHgZ5hPXFDHXXm8n/+A2MqMHTIUNj0elT+49XH17dKGVLWrhPhdeoRFoZxFdOtJSU4XzF+8uSb4VfHMaqyFz5/QT4PGTIE12WUI2DwIHSpp25ffPc9mJCHEcOHo2O1+UR3g4sVwzNmzIB3RWLokLRuu3zu1KlTlW069+JLEE0ioi4ET56M1mLOzsaFiuGp06bBN9SeUmUdOoxikWD3qvszq2L/KRRVNFyKf2Brq2MO+Zcuyc+3a5cuGDNrVs19SdpbQJmoC0MwdGLb7WMyd+yEFifRp08EQq9xX2ewGPA/n/+PM1/wXfU+zLDIbR9SsUz98eOo2UnFXhemDe6OxOOJKDOXoa+p9v6yQUH2H0tBQUEutU92qUTWkbxWpvzKpV9Eq614EBEREZFncanLb4WHh8suAhaLxZnEilZa0SpLREREROSyiWz37t0xevRoeTktR7cB0aWgrm4FREREROS52iSRfeKJJ2QfVdH6KvpwDRgwwDlt9erV8iEuv/XGG2/IS2sREREREblEH9kPP/xQPmozePBgeRZsS4mOjpYPcUYvEREREamXS3UtaA3z5s1DVFSUPKOXiIiIiNTL4xJZIiIiInIPTGSJiIiISJWYyBIRERGRKjGRJSIiIiJVYiJLRERERKrkcreobWm8/BYRERGRe/C4FllefouIiIjIPXhcIktERERE7oGJLBERERGpEhNZIiIiIlIlJrJEREREpEpMZImIiIhIlbw88fJb9957LwwGA1xRYpYWeWXltU4L8vOu8jqw2msich0+Xhr4eXthwZpjKDVa2qQMVpsinx/8NBZWxT7szlLydVWeXV1SlhbT/m8fNh5NhbtYHXNRblPM+Ty4kiKdCa//bMFD/4l1fi9qk1Gkx+z3DmDpFyehVh/vv9ikfU6R3gw18bhE1tUvv9UpyBd6k7XWaW/NHYGb+3eVw7OvD8VLd1zfyqUjooYK8vPB9qU3yx+nhTpTmwTObLXJ54QrJbBUDLuz3FJ7I0BdjQGuJqPIgJQCPY5dKoC7+OlSgdymE6lFcCVZJUaklAIHzufBYqv7u3ApT4ek7FLsO5cLtTqRVgRbE364FrfR/upaedwNEVxdgG/draxdgv2B9v5yuEfHQCDItxVLRkSNNTC0Pbw1GgaOiKiFeFyLLBERERG5ByayRERERKRKTGSJiIiISJWYyLoAk/Vqx2qjtaxNy0JE7scrIKPW8fmGfMSkx8hHclFylWl5igUxgQGI0WegpLwEBzIOIKssq8Yy4vPjncspt5bDpthPpCk1l8lx5wrP1bru1JJUaE1a52uLzVxj3lJTqVyGmM/xXN0veWcaFAOxfXHZcWgKc6V9df3zmWW8cnQ5tU5P0V5Ga19E4nTeaSTkJzR4fvFZnMg5IYcdn2/lY1Vzyy7LlusRjuccr7PeVKa36HG55PI1rS9Nm4YjmnI4PgaxbWIbzxT9BI1voRyXUJAg49YQYt5TuafQVOL7czjzMNK16fJ1YkGi8zsqHqJeifpVH6tXAXRe9i27bC6Bzlz3VTy8gy4joegYio3FSCtNgxrxZC8XkFSUhD4Vw4WmbFxnmSC+1m1cKiJSo6Gdh2JYl2EY1X0UfL18YdZej8Dw9biir3rS2ahuo/DNhW+w6uQqmG1mZOmyELsg1jn9H0oeErt2hjn/J5RvmSUPhmO6j8H636x3zjMjfAbejH0TK4+vlNOXjVmGfnnncZ24goA+F6uOr0RGaQaOP3i8Rjl/SPkBO092wV9wo3xdUq7FE9vnI25BHLy97Ce9bj6/GZ/k/YJ2vu3k8n9fPBy/rbacpw48hQf7Lf/VuCz4fgEMFgO+MvfFtZ5+l1iY6NxX1yfqfBTeiH0Dk8Im4d+3/rvKtECfQKy7+C366IcCwfar0FyLXP3Vy1uVmXXoiE51zputy8bC7xfCW+ONLxX7CcP1EfVh/vb58vmbu7/Buyfexf70/fjb2L/hD8P+gJZwJOsINhx9FUtGLcHvf/y9rLuV60JtRN16e+cfsXve7kav74k9TyDFtwSrAgMxDcAXSV9g9S+r4acJRmAvDZD9OBZsXwCrYsWuubtwXTtRq2tXZipzzvvjnB/RM7gnrtXetL342/6/YUCnAdh0+yY8sP0B9OnYR352whXdFSweuRgLIxfWuYzi9muQ7m+TNWJb2SUcOP1vPDnuyVrn9e98AGuTd2PjJS8EeAfguYnPIRuHoSZskXUB1ooWDCHYpxMGl49t0/IQkXpFdIjAF3d8gafHPy2TAGPmg4DNDzal6mX9ru96PaLujMLmuzbjo5kf1WhtM8GGxUUlmNN+gLNFR7S4VvbCjS/Ig6xjuliGxWZfT/+O/bD61tUw2epuxau+TpE4VWax2a+F6Vi+Wam9Jcqi/Po1Mx3rUpxtcE3bV9fHEafq8RJC/EPQp0OETHqaQrRgO1T/bKtzxNW+zgZsv3L1PeLzc8Sutu1pTmL55bbyWutCXa61ldj5GWmuLmdar2mY328ZoLEAGpvzM/rVFlDF2uB5G1wuazkURZF1W/x4FN9T8Zjcc/Kvfw6aqvWhvvmNubejb/tI+R2bP2Q+bo24FWrj44k3RBAPV70hAhERERE1jMe1yLr6DRGIiIiIqGE8LpElIiIiIvfgcV0L1GCIKYE/MYioVhrYgLSjgG8QUF5qH6nLA+K3An0mA6mHgB6jgM59ne/xgs3eMzLlMFBUcSZ9/+lAYAjk6fOXD9k7Rmb8DJRrgY69gdr6HhpL7OvpGG5/nbTdPs4TyZhVSN4JGHoAg38LiJOTrBV9di3G2t8r+lHqdfAKrqVv76UYwCcA6C1O+m0jKZW2rfJ2iHJbTMC57wHRX7jbECA0spnXXelEoysn7PVrwMyGvTc30f7QeNk/i5Yk+oKL+i9i0CG0avlHRADXcke//AuAMaL2z2NQE7fHZgNSjtQYPVxzEVmOPrS5Z+X3O6jsEtSEiayL8baW43ZdNHRj/gjUdqvnQbOAosvAgFvaoHRE1BxS248Geo0Hht5Z/4z9ZwDpx+wJp0iW2nVDvm84uv9nNuDlA4SNAvw7ALtfticWIgESSUfEzcAj252LaQcjysQfcEnbAHHWenE6MOx3wOzXgSsngW8eB3qFAZ/eBoiTVsRyevQAQqodVMW+59C79gNeRBgQ+wngrxPZdZsKMmS27gpFwhH9IICKBObUJiAuGbjnIyDybiCj4jJfBRdqf782EyjQome7EMCv0sm9RSnAhrvsidgzzXgppNLKV8H5lZO9tFeAjfcAfXtfHZdXcSks8UNH2QTsehFoHwaIk/CWNezyZw2iLwBi/wfo2cP++j+/AcRJX48dAHqM/PX3b7rP/gOv9ApgKEKLSvgvsOVRoPv1wKM/XB2/bSnQeSAQPr7xyxTf8TP2S39JJr39+csHgCeOA136X3t5z/8AHHhLnFJeZfTjPtvwTl4nINDPvq9JTUBIfs3L7KHvVOTkdYcrYtcCl6Mgxacfim58pvbJw+cCi3bbE1oiUqWsdpHAol3AjYvrn1H8YBXf9xH32V/7BuGXQcvsyYg4wN+1Cmgfak9iq7Sc1XEm9+w37Msbef/VeSqfZe04+10sRxw021ckFA4iwb1/E1BxNQH87hOgcz+0NU0TrwDQaCL2PkFXX89bB4QOuxrLiis31Hmx2IrRXtXL7WjJFZ+nYxnNwbHcSuuue95azrp31C9RJjE9YpK97jXxDP061+PguHJBQ9cj5rvrffsPueYuW23rquu71pTr7Vb+3CvHo6nbYzUBIbVfPE6DinWOfUTuH4q6jqs509w10HlVTYJdBRNZIiIiIlIlJrJEREREpEpMZImIiIhIlZjIEhEREZEqedxVC3hnLyIiIiL34HEtsryzFxEREZF78LhEloiIiIjcAxNZIiIiIlIlJrJEREREpEpMZImIiIhIlZjIEhEREZEqMZElIiIiIlViIktEREREqsREloiIiIhUiYksEREREakSE1kiIiIiUiUmskRERESkSj7wMNHR0fJhMBjauihERERE1AQe1yI7b948REVFITAwsK2LQkRERERN4HGJLBERERG5ByayRERERKRKTGSJiIiISJWYyBIRERGRKjGRJSIiIiJV8rjLb6mB2arghejT8PHSwKYosCmArzd/cxCpka+3BgYz4KWB/C57e2nw7ekrOJFWhIn9uuCXjGKEhwRBZ7LiLbMV4noqL36bgHg/X7xwRyRG9w5BXlk5ugEo0puxOt6KmddYliK9qcHzFuhMSMw3AKGdnOMUAMu+OoW3G7ne5VGnkF9mwqr7R6O1mSw2LPvqJKyKKH39jGarc/jNH5MwKqcUvRu4nmytEZt2nYNvbjKS0oqAYMBktdWYT2s0O9eToy2HVxf7+LzScrwRfQrv1LH8A+fz8O7u81g4IQJzxvZCY5RbapbjWmWVGJCeUogtub/geaMFwYqCJ6NO43KBDv4+XijSmWVd/youXdb1mPN58jg2vp0F9zjKY7bCF81rRfRpPK0rx7tfn8FzHS0IauLyLFbFmSAZzJZrWsa+c7l4f08yHr6xD+4Z3bNiuTb8Leo09OUW/Gv+aAT7+yCjyIAOFe/JKjZAFL5Yb65SZ3VmC9oBct5j51Lx3cHDja4LFrEDqsfRSwX48fBhTMzTIbLatLd3nENKgQ7QwOUwO3JBRotVVpivn5iEzY/fhE2LJqBvV1GFiUhtNv1xItb+/gaMjQiRrxdP64+X77weI3t1wsf7L+LwhQJ8GZeObaevyB+vwm+H9YCXRoOjlwrl69RCvXwuNZoRX/jrCVl1moqDT3pRw6+fXWq0QFduQYnx6kFcHFdjL9vL1BhbT2TKRCy1UIfWJmL2/ZlsWfaGJL0O3566grJyS6N+JKTk6/HOrvPOhgdzLYms3mSViZ0Q0s4PT0wfIIcv5JbhRFpxncs/mJyHk2nF2JOUg8YSSZFD42tPVWmFethsCsZFhMBgtsJqU7D1ZCbmjQ1HeqEB53JK8eRtgzEqvBNW7jqPcosVM4eGYsuJDOcytIZrSwzrI5Yf6OuNIp0Jpc2wfIPl6o+aQl3DfwBWJuq8/TPLvbpcs1V+18W47BKjHJdTan92/CASxHdP5AIO+dpy+7xaIzoF+sqcoLF1wVxLfazs8IV8mVj371Yz3/gxPhvPzB7ikkmjK5aJAAT5+WBYz44Y0zsENw3oypgQqZT4Hk8f0h2aiqaM9gE+uH1ED4zufbWl08HH2z7PxH6dEdapOa917YLNKG4syM+7QfOJpEHUB7Xx9/XG8F4dq4ybGdkdHQLt7awhQX4YGBosh0PbB+DmVjqGiXKJmLq7bu0DENnD0YbbvCK6BOG6DgG1Truhb2e4IiayRERERKRKTGSJiIiISJWYyBIRERGRKjGRJSIiIiJVYiJLRERERKrERJaIiIiIVMn9r1NRTXR0tHwYDA2/niIRERERuR6Pa5GdN28eoqKiEBjYnNdoJCIiIqLW5nGJLBERERG5ByayRERERKRKTGSJiIiISJWYyBIRERGRKjGRJSIiIiJVYiJLRERERKrERJaIiIiIVImJLBERERGpEhNZIiIiIlIlJrJEREREpEpMZImIiIhIlZjIEhEREZEqMZElIiIiIlViIktEREREqsREloiIiIhUiYksEREREakSE1kiIiIiUiUmskRERESkShpFURR4IH9/f3Tr1q3V1ldWVobg4OBWW58aMCaMCesJvzfcl3D/ymNO2ylTQW6Sl5eH8vLyOqd7bCLb2nr16oWMjIy2LoZLYUwYE9YTfm+4L+H+lcecttPLDXITdi0gIiIiIlViIktEREREqsREtpUsX768tValGowJY8J6wu8N9yXcv/KY03aWu0Fuwj6yRERERKRKbJElIiIiIlViIktEREREqsREtoUlJyfjpptuwqBBgzB+/HicPXsWnqBPnz4YMmQIRo0aJR9fffWVHJ+bm4vZs2dj4MCBGDZsGA4dOuR8j16vx/z58zFgwAAZr61bt0LNli5dKuOg0WgQHx/vHH+tMbDZbPjLX/6C/v37y+kfffQR3CUm06ZNQ79+/Zz15d133/WImBiNRtxzzz1yu8R2i3qRkpLi0fWkvph4aj0RbrvtNowYMUJu9+TJk3Hq1CmPrid1xcOT64jDK6+8UmUf6/Z1RFxHllrO9OnTlbVr18rh6OhoZeLEiR4R7oiICOXMmTM1xj/yyCPKSy+9JIdjY2OV3r17K2azWb5+5ZVXlIcfflgOX7p0SQkNDVUKCwsVtYqJiVHS09NrxOJaY7B+/XplxowZisViUQoKCuRyExMTFXeIydSpU5Vt27bV+h53jonBYFC2b9+u2Gw2+XrVqlXKrbfe6tH1pL6YeGo9EYqKipzD//3vf5XRo0d7dD2pKx6eXEeE48ePK7Nnz5b1wLGPdfc6wkS2BeXk5CgdO3Z0VhixYxaV5PLly4qnJrLt2rVTcnNzna9vuOEGZd++fXI4MjJSfskc5s2b5/wR4E6xuNYY/Pa3v1WioqKc01asWOHcOalNYxJZT4mJEBcXp/Tv318Os57UjAnrid26deuUsWPHsp7UEg9PriNGo1E2lomEtPI+1t33Jexa0ILS09MRFhYGHx8f+Vo09ffu3RtpaWnwBAsWLMDw4cOxaNEieYu5goIC+VdF5VsDi7+ZHfEQzxEREbVOcxdNiYG7x2fFihWyvtx33324dOmSc7wnxeT999/HnXfeyXpSS0wcPLmePPTQQwgPD8fzzz+P9evXe3w9qR4PT68jL774IhYuXIi+fft61DGHiWwLE8lrZZ5yR+ADBw7g9OnTOHHiBLp06YKHH364QfGoPN1dY9WUGLhrfDZu3IjExET88ssvsr/bHXfc4XExee2112Sf+n/+85/yNetJzZh4ej3ZsGGDbCB59dVXZbLm6fWktnh4ah356aefEBcXh8WLF9eY5u51hIlsCxK/FMU9jC0Wi7MSiC+daJV1d45t9PX1xbJly3Dw4EGZ0AqiddYhNTXVOa94dpzUUX2au2hKDNw5PuK74thpLlmyRLaiiJYET4nJ22+/LU+y+OGHHxAUFMR6UktMBE+vJw6iYWDfvn3O156+P3HEQ9QFT60jMTExSEpKkq2xouVU5B6zZs1CbGys+9eRtu7b4O5Ef53KJ3tNmDBBcXdlZWVVOuK/8847yuTJk+Ww6FReudN5eHi4sw+xGF+503n37t1lB3O1q94f9FpjIOrRLbfc4ux4Lzrsnz17VlF7TMS2Z2dnO6dt3rxZbpuDu8dEfD/GjBlT48RGT64ntcXEk+tJSUmJkpmZ6Xy9detWpWfPnvK8C0+sJ3XFw2QyeWwdqW8f6+51hIlsC0tKSpKdrwcOHCg7o8fHxyvu7uLFi8qoUaOU4cOHK8OGDVPuuusu5wluYicjzkAeMGCA7GS+f//+KgnwvffeK0/sEPESib+aLV68WO5cvb295Ul+jhNWrjUGYmciltmvXz/5EGdzu0NMxDaL74aoKyNGjJBnyZ46dcojYiKu4CDaE0TZR44cKR/jx4/36HpSV0w8uZ6kpaXJE3Qc2y6Si5MnT3psPakrHp5cR+pLZN29jvAWtURERESkSuwjS0RERESqxESWiIiIiFSJiSwRERERqRITWSIiIiJSJSayRERERKRKTGSJiIiISJWYyBIR/Qpxp5whQ4Zg1KhRzsfZs2dVE7d169Zh7ty5cAWuVBYiUj+fti4AEZEabN68GcOGDWuRZYvbWPv4cHdMRNRYbJElImoCcU/3N998ExMmTJD3OV+7dq1zWnJyMm6//XbccMMNGDlyJD766KMq73vnnXcwbdo0PPvss8jMzMQtt9yC66+/HnfccYd8fPDBB7hy5QpCQ0Oh1+ud750/fz4+/vjjGmUxmUxYsWIFhg8fLtc3e/Zs57TS0lL5PjFt3Lhx8h70QnZ2NqZPn46xY8fKdS9dulTc8VFOe/nll/HAAw/gzjvvRGRkJGbMmIHCwkJny6q4l3ttyxQ2btwoYzJmzBhMnToV8fHxrGdE1OzYBEBE1ADi7/CAgADn69jYWPj5+clhMf7YsWNITEzE+PHj8eCDD8pEVSSBIqET3RJEIjpx4kT5EMmdUF5ejv3798vhOXPmyITy+eefR1pammz9FYloWFgYZs6ciU2bNmHRokUy8dy9ezc++eSTGmV8/fXXcfHiRfz888/w9/dHXl6ec5oo3+nTpxEREYFnnnlGJt+rV69Gp06dsG3bNgQHB8NqteLuu+/Gli1bnH//i/fFxcWhc+fOuP/+++V7ROJd3zIPHz6ML7/8EgcOHJDlOHjwIBYsWCDnJSJqTkxkiYia2LVAJGnC0KFDZRcBkWxqtVokJCTI5K9yq6joW+tIZP/whz84p+3btw/vv/++HO7du7dsnXX461//iscee0wmsiJRFAmySDyr++6772Qrr0gehW7dujmn3XzzzTLhFG688UasWrVKDttsNjz99NM4dOiQbInNzc2VfYAdiexvfvMbmcQ63nfmzJlfXeY333wjk1bRIusgkmrRYkxE1JyYyBIRNVHlllpvb2/Z51UkhV27dsWpU6fqfF/1ZFS04tZGtPKKdcTExMiW2L179zZLGYWVK1eioKBAtq6KeZYvXw6j0fir76tvmth2kaT/4x//aHQ5iYgag31kiYhawODBgxEUFIQNGzY4x124cMHZx7Q60VdW9DsV0tPTaySrolV24cKFsh/roEGDal3GXXfdhffee092WRAqdy2oS1FREa677jqZlObk5CA6OhpNJfrUiu0W2+Fo9RXdHYiImhsTWSKiBhB/tVe+/Jbo91kf0cVA9D2NiorCiBEjZAIqugYYDIZa5//Xv/6FXbt2yZO0RKvopEmT0LFjxyrrLysrw5IlS+pcp+gi0L9/f4wePVqW8eGHH/7V7RIndx05ckTOL1pRRX/cppoyZQpee+012d9WbI/okvHVV181eblERNVpFMfpqURE1GZEguvr6ysT4KysLHmlgz179siWXcfJZaJFNikpCV5ebIMgIhLYR5aIyAWIS3U99NBDsn+p2WzGSy+95ExiRUvuzp07sWbNGiaxRESVsEWWiIiIiFSJ/08RERERkSoxkSUiIiIiVWIiS0RERESqxESWiIiIiFSJiSwRERERqRITWSIiIiKCGv1/Xb7+cmsGpqcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [286.980, 179.172, 161.187, 97.309]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['0', '50', '127', '255']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend(title=\"cmis_detector_voffset\")\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('QC background spectra, SIPHRA-Ch.2 SiPM-Chs.5-8')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/260209_Cs137_BGsubtraction.ipynb b/notebooks/260209_Cs137_BGsubtraction.ipynb new file mode 100644 index 0000000..8a9508c --- /dev/null +++ b/notebooks/260209_Cs137_BGsubtraction.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-09T14:07:23.324172Z", + "start_time": "2026-02-09T14:07:23.320337Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75bebada911337a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-09T14:02:27.067251Z", + "start_time": "2026-02-09T14:02:24.867078Z" + } + }, + "outputs": [], + "source": [ + "BITS_12 = 2**12\n", + "N_BINS = 512\n", + "\n", + "dfs_SiPM58_BG = []\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/1_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "times_SiPM58_BG = [119.634, 161.062, 148.920, 164.828]\n", + "\n", + "dfs_SiPM14_BG = []\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "times_SiPM14_BG = [34.836, 80.792, 54.386, 90.837]\n", + "\n", + "dfs_SiPM14_Cs137 = []\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/12_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/11_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/10_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/9_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "times_SiPM14_Cs137 = [23.381, 24.606, 24.144, 24.645]\n", + "\n", + "dfs_SiPM58_Cs137 = []\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/13_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/14_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/15_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/16_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "times_SiPM58_Cs137 = [24.720, 24.916, 24.848, 24.934]\n", + "\n", + "SIPHRA_Ch_list = ['Ch2', 'Ch6', 'Ch10', 'Ch14']\n", + "dfs = [dfs_SiPM14_BG, dfs_SiPM14_Cs137, dfs_SiPM58_BG, dfs_SiPM58_Cs137]\n", + "data_SiPM_chs = ['1-4', '1-4', '5-8', '5-8']\n", + "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "436876e4-6c49-4cc6-9552-6551a7059e27", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-09T14:04:20.965711Z", + "start_time": "2026-02-09T14:04:20.634259Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "for idx,(data, ch) in enumerate(zip(dfs_SiPM14_Cs137, SIPHRA_Ch_list)):\n", + " plt.hist(data[ch], N_BINS, range=(0,BITS_12), weights=(1/times_SiPM14_Cs137[idx])*np.ones_like(data[ch]), log=True, histtype='step', label=legend[idx])\n", + "plt.legend(title=\"SIPHRA active channel\")\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title(r'$^{137}$Cs spectra, CC, SiPM Ch.1-4')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8127dbeb-d8f3-44bc-abd2-4a164c19731d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10), dpi=80)\n", + "for idx,(data, ch) in enumerate(zip(dfs_SiPM58_Cs137, SIPHRA_Ch_list)):\n", + " plt.hist(data[ch], N_BINS, range=(0,BITS_12), weights=(1/times_SiPM58_Cs137[idx])*np.ones_like(data[ch]), log=True, histtype='step', label=legend[idx])\n", + "plt.legend(title=\"SIPHRA active channel\")\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title(r'$^{137}$Cs spectra, CC, SiPM Ch.5-8')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "926fc8c293cea7a4", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-09T14:04:23.603853Z", + "start_time": "2026-02-09T14:04:23.361384Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10), dpi=80)\n", + "for idx,(data, ch) in enumerate(zip(dfs_SiPM14_BG, SIPHRA_Ch_list)):\n", + " plt.hist(data[ch], N_BINS, range=(0,BITS_12), weights=(1/times_SiPM14_BG[idx])*np.ones_like(data[ch]), log=True, histtype='step', label=legend[idx])\n", + "plt.legend(title=\"SIPHRA active channel\")\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Background spectra, CC, SiPM Ch.1-4')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "710025b1f4f33391", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-09T14:21:34.842292Z", + "start_time": "2026-02-09T14:21:34.835721Z" + } + }, + "outputs": [], + "source": [ + "H_bg, bins_bg = np.histogram(dfs_SiPM14_BG[0]['Ch2'], bins=N_BINS, range=(0,BITS_12))\n", + "H_cs, bins_cs = np.histogram(dfs_SiPM14_Cs137[0]['Ch2'], bins=N_BINS, range=(0,BITS_12))\n", + "H_cs_subt = (H_cs/times_SiPM14_Cs137[0] - H_bg/times_SiPM14_BG[0]).clip(min=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "233314b3cd8deb29", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-09T14:39:03.778602Z", + "start_time": "2026-02-09T14:39:03.180322Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10), dpi=80)\n", + "plt.bar(bins_cs[:-1], H_cs_subt, width=BITS_12/N_BINS, log=True)\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/260212.ipynb b/notebooks/260212.ipynb new file mode 100644 index 0000000..b058d9a --- /dev/null +++ b/notebooks/260212.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-13T09:12:20.943604900Z", + "start_time": "2026-02-13T09:12:20.924522400Z" + } + }, + "source": [ + "import sys\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ], + "outputs": [], + "execution_count": 38 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Testing the new channels\n", + "\n", + "We recently got 4 new SiPHRA channels, so now we have connection to\n", + "channels 2, 4, 6, 8, 10, 12, 14, and 16. We connected each channel to SiPM\n", + "ch 5-8 and tested them individually with only background radiation. Then\n", + "we did the same test, but with a Cs-137 source. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
Parameter
Value
Charge comparator threshold (active channel)
20
Charge comparator threshold (inactive channels)
255
" + ], + "id": "f22322a36b757cda" + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "BITS_12 = 2**12\n", + "N_BINS = 512 " + ], + "metadata": { + "collapsed": false + }, + "id": "2dd4b1b5058ccc07", + "execution_count": null + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "#Importing background data\n", + "dfs_bg = []\n", + "dfs_bg.append(pd.read_csv('../data/260212/1_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch2_QT_thr20_background.csv')) \n", + "dfs_bg.append(pd.read_csv('../data/260212/5_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch4_QT_thr20_background.csv'))\n", + "dfs_bg.append(pd.read_csv('../data/260212/2_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch6_QT_thr20_background.csv'))\n", + "dfs_bg.append(pd.read_csv('../data/260212/6_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch8_QT_thr20_background.csv'))\n", + "dfs_bg.append(pd.read_csv('../data/260212/3_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch10_QT_thr20_background.csv'))\n", + "dfs_bg.append(pd.read_csv('../data/260212/7_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch12_QT_thr20_background.csv'))\n", + "dfs_bg.append(pd.read_csv('../data/260212/4_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch14_QT_thr20_background.csv'))\n", + "dfs_bg.append(pd.read_csv('../data/260212/8_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch16_QT_thr20_background.csv'))\n", + "\n", + "background_times = [158.063, 139.765, 174.846, 128.852, 164.299, 180.618, 132.460, 156.538]\n", + "\n", + "single_ch_spectra_bg = [df[ch].tolist() for df, ch in zip(dfs_bg, ['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16'])]" + ], + "metadata": { + "collapsed": false + }, + "id": "75bebada911337a", + "execution_count": 39 + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plotting background spectra\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.2', 'Ch.4', 'Ch.6', 'Ch.8', 'Ch.10', 'Ch.12', 'Ch.14', 'Ch.16']\n", + "for idx,s in enumerate(single_ch_spectra_bg):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/background_times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Charge comparator background spectra, SiPM-Channels 5-8')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "#plt.savefig('../figures/QC_SiPMch5-8_8channels_bg.png')\n", + "plt.show()" + ], + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-13T09:33:47.318900500Z", + "start_time": "2026-02-13T09:33:46.027690500Z" + } + }, + "id": "cb4c1aad-40db-456b-a316-8ce713563841", + "execution_count": 44 + }, + { + "cell_type": "code", + "id": "e68ab9ee-bdeb-4c34-b3ef-e032a4b3a6ec", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-13T09:12:32.032627Z", + "start_time": "2026-02-13T09:12:28.650229500Z" + } + }, + "source": [ + "#Importing 137Cs data\n", + "dfs_Cs = []\n", + "dfs_Cs.append(pd.read_csv('../data/260212/9_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch2_QT_thr20_Cs137.csv')) \n", + "dfs_Cs.append(pd.read_csv('../data/260212/10_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch4_QT_thr20_Cs137.csv'))\n", + "dfs_Cs.append(pd.read_csv('../data/260212/11_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch6_QT_thr20_Cs137.csv'))\n", + "dfs_Cs.append(pd.read_csv('../data/260212/12_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch8_QT_thr20_Cs137.csv'))\n", + "dfs_Cs.append(pd.read_csv('../data/260212/13_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch10_QT_thr20_Cs137.csv'))\n", + "dfs_Cs.append(pd.read_csv('../data/260212/14_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch12_QT_thr20_Cs137.csv'))\n", + "dfs_Cs.append(pd.read_csv('../data/260212/15_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch14_QT_thr20_Cs137.csv'))\n", + "dfs_Cs.append(pd.read_csv('../data/260212/16_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch16_QT_thr20_Cs137.csv'))\n", + "\n", + "Cs137_times = [25.497, 24.604, 24.756, 24.517, 24.732, 24.813, 24.564, 24.710]\n", + "\n", + "single_ch_spectra_cs = [df[ch].tolist() for df, ch in zip(dfs_Cs, ['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16'])]" + ], + "outputs": [], + "execution_count": 42 + }, + { + "cell_type": "code", + "id": "97eb2049-7aab-4804-a96b-555d5d4c84f9", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-13T09:12:36.729790900Z", + "start_time": "2026-02-13T09:12:32.029126600Z" + } + }, + "source": [ + "#Plotting 137Cs spectra\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.2', 'Ch.4', 'Ch.6', 'Ch.8', 'Ch.10', 'Ch.12', 'Ch.14', 'Ch.16']\n", + "for idx,s in enumerate(single_ch_spectra_cs):\n", + " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/Cs137_times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Count rate ($s^{-1}$)')\n", + "plt.title('Charge comparator 137Cs spectra, SiPM-Channels 5-8')\n", + "plt.xticks(np.arange(0,4500,500))\n", + "plt.grid()\n", + "#plt.savefig('../figures/QC_SiPMch5-8_8channels_137Cs.png')\n", + "plt.show()" + ], + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 43 + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/CT_Cs137_calib.ipynb b/notebooks/CT_Cs137_calib.ipynb new file mode 100644 index 0000000..d72440e --- /dev/null +++ b/notebooks/CT_Cs137_calib.ipynb @@ -0,0 +1,731 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "1b3d1721-b1d7-4c84-aa16-7d0dd20c2916", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition, fit_peak_expbg\n", + "\n", + "# ROOT.gROOT.SetBatch(False)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", + "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8d28291e-86ef-46db-903e-fc8a7140859f", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d0ef0cc0-2107-4ad2-ace8-04c4abf3ed95", + "metadata": {}, + "outputs": [], + "source": [ + "BG_PMchs58 = []\n", + "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/1_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=2,\n", + " exposure_sec=119.634,\n", + " sipm_chs=\"5-8\"))\n", + "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=6,\n", + " exposure_sec=161.062,\n", + " sipm_chs=\"5-8\"))\n", + "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=10,\n", + " exposure_sec=148.920,\n", + " sipm_chs=\"5-8\"))\n", + "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=14,\n", + " exposure_sec=164.828,\n", + " sipm_chs=\"5-8\"))\n", + "\n", + "BG_PMchs14 = []\n", + "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv',\n", + " active_chs=2,\n", + " exposure_sec=34.836,\n", + " sipm_chs=\"1-4\"))\n", + "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/6_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=6,\n", + " exposure_sec=80.792,\n", + " sipm_chs=\"1-4\"))\n", + "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/7_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=10,\n", + " exposure_sec=54.386,\n", + " sipm_chs=\"1-4\"))\n", + "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/8_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Background.csv',\n", + " active_chs=14,\n", + " exposure_sec=90.837,\n", + " sipm_chs=\"1-4\"))\n", + "\n", + "Cs137_PMchs14 = []\n", + "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/12_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=2,\n", + " exposure_sec=23.381,\n", + " sipm_chs=\"1-4\"))\n", + "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/11_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=6,\n", + " exposure_sec=24.606,\n", + " sipm_chs=\"1-4\"))\n", + "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/10_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=10,\n", + " exposure_sec=24.144,\n", + " sipm_chs=\"1-4\"))\n", + "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/9_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=14,\n", + " exposure_sec=24.645,\n", + " sipm_chs=\"1-4\"))\n", + "\n", + "Cs137_PMchs58 = []\n", + "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/13_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=2,\n", + " exposure_sec=24.720,\n", + " sipm_chs=\"5-8\"))\n", + "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/14_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=6,\n", + " exposure_sec=24.916,\n", + " sipm_chs=\"5-8\"))\n", + "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/15_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=10,\n", + " exposure_sec=24.848,\n", + " sipm_chs=\"5-8\"))\n", + "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/16_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv',\n", + " active_chs=14,\n", + " exposure_sec=24.934,\n", + " sipm_chs=\"5-8\"))\n", + "\n", + "datasets_lsts = [BG_PMchs58, Cs137_PMchs58, BG_PMchs14, Cs137_PMchs14]\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kCyan, ROOT.kYellow, ROOT.kSpring, ROOT.kViolet]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fb43e56f-ec35-4690-b0f1-2cef16671a15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv\n" + ] + }, + { + "data": { + "text/plain": [ + "array([137., 135., 135., ..., 135., 136., 135.], shape=(99976,))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_ds = datasets_lsts[2][0]\n", + "print(current_ds.filepath.name)\n", + "current_ds[16]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "953f73d6-0458-42d5-9cdd-96676945177a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " legends_raw[-1].Draw()\n", + " canvas_raw[-1].Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d66dc8c8-bbac-40db-934c-b55071da1c65", + "metadata": {}, + "outputs": [], + "source": [ + "# if ROOT.gROOT.FindObject(\"canvas\"):\n", + "# ROOT.gROOT.FindObject(\"canvas\").Close()\n", + "# canvas = ROOT.TCanvas(\"canvas\", \"canvas\", 800, 600)\n", + "# ROOT.gStyle.SetOptStat(11)\n", + "# ROOT.gStyle.SetStatFontSize(0.03)\n", + "# ROOT.gStyle.SetStatW(0.15)\n", + "# legend_ary = ROOT.TLegend(0.65, 0.7, 0.8, 0.88)\n", + "\n", + "# Yinit = 0.82 # For stat boxes\n", + "# hists_58BG = []\n", + "# current_dataset = datasets_lsts[2]\n", + "# for acq, color in zip(current_dataset, colors):\n", + "# hist = ROOT.TH1F(f\"h58bgCh{acq.active_chs[0]}\", \"\", BITS9, 0, BITS12)\n", + "# hist.Fill(acq[acq.active_chs[0]])\n", + "# hist.Scale(1/acq.exposure)\n", + "# # Preeting up...\n", + "# hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + "# hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + "# hist.SetLineColor(color+2)\n", + "# legend_ary.SetHeader('SIPHRA channel')\n", + "# legend_ary.AddEntry(hist, f\"Ch. {acq.active_chs[0]}\", 'l')\n", + "# hists_58BG.append(hist)\n", + "# hists_58BG[-1].Draw(\"sames hist\")\n", + "# canvas.SetLogy()\n", + "\n", + "# ROOT.gPad.Update()\n", + "# Yinit = 0.82\n", + "# for i, sp in enumerate(hists_58BG):\n", + "# st = sp.FindObject(\"stats\")\n", + "# print(type(st))\n", + "# st.SetY1NDC(Yinit - i*0.08)\n", + "# st.SetY2NDC(0.1)\n", + "\n", + "\n", + "# legend_ary.Draw()\n", + "# canvas.Draw()\n", + "# ROOT.gPad.Modified()\n", + "# ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7240bc8-54cd-4af5-9187-99770b47829c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ead8479a-3d00-4ccd-980c-dc6c12af58b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "864.490254507108\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.454998\n", + "Chi2 = 0.909996\n", + "NDf = 71\n", + "Edm = 4.19258e-06\n", + "NCalls = 254\n", + "Const = 55.1644 +/- 67.0627 \n", + "Denom = 212.972 +/- 54.3249 \n", + "Norm = 0.990514 +/- 0.486902 \n", + "Mean = 863.094 +/- 44.5963 \n", + "Sigma = -68.8679 +/- 41.554 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.361677\n", + "Chi2 = 0.723354\n", + "NDf = 71\n", + "Edm = 9.93854e-07\n", + "NCalls = 237\n", + "Const = 58.8242 +/- 68.9613 \n", + "Denom = 214.571 +/- 52.9045 \n", + "Norm = 0.986847 +/- 0.505386 \n", + "Mean = 861.59 +/- 46.1117 \n", + "Sigma = -65.6188 +/- 43.9628 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.363845\n", + "Chi2 = 0.72769\n", + "NDf = 71\n", + "Edm = 1.69107e-07\n", + "NCalls = 242\n", + "Const = 66.9958 +/- 59.986 \n", + "Denom = 212.005 +/- 43.9622 \n", + "Norm = 0.905532 +/- 0.437406 \n", + "Mean = 900.19 +/- 43.1657 \n", + "Sigma = 68.4399 +/- 39.4644 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.321441\n", + "Chi2 = 0.642883\n", + "NDf = 71\n", + "Edm = 2.84474e-06\n", + "NCalls = 284\n", + "Const = 53.0888 +/- 62.0095 \n", + "Denom = 219.835 +/- 55.2175 \n", + "Norm = 1.02893 +/- 0.500587 \n", + "Mean = 864.49 +/- 44.1728 \n", + "Sigma = 67.5654 +/- 42.4445 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Deleting canvas with same name: canvas\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "canvas = ROOT.TCanvas(\"canvas\", \"canvas\", 800, 600)\n", + "for hist in hists_58BG:\n", + " fit_fn, fit_result = fit_peak_expbg(hist=hist, name='k40peak', xl=700, xr=1300, norm=1, mean=870, showFit=True, keep_prev_fncs=False)\n", + " # hist.Draw('sames hist')\n", + "for hist in hists_58BG: # If this is inside the first for loop, nothing is plotted\n", + " hist.Draw('sames hist')\n", + "canvas.SetLogy()\n", + "canvas.Draw()\n", + "print(fit_fn.GetParameter('Mean'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "836c07b7-c48f-4138-98e9-014138705c4a", + "metadata": {}, + "outputs": [], + "source": [ + "for element in hists_raw_dict.values():\n", + " print(element)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ca2353c-1411-45fb-b29a-5c8057105833", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import namedtuple\n", + "\n", + "BGPeaks = namedtuple('BGPeaks', ('k40', 'cs137'))\n", + "\n", + "for hist_list in [hists_raw_dict[hists_58BG], hists_raw_dict[hists_]:\n", + " for hist in hist_list:\n", + " fit_peak_expbg(hist=hist, name='k40peak', xl=700, xr=1300, norm=1, mean=870, showFit=False, keep_prev_fncs=True)\n", + " fit_peak_expbg(hist=hist, name='cs137peak', xl=1200, xr=1800, norm=0.3, mean=1400, showFit=False, keep_prev_fncs=True)\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/QT_compare_channels.ipynb b/notebooks/QT_compare_channels.ipynb new file mode 100644 index 0000000..c7e14aa --- /dev/null +++ b/notebooks/QT_compare_channels.ipynb @@ -0,0 +1,532 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "39965dbb-a83f-4f1b-b012-9ea2c0e0c583", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "08bef2cf-8af2-4047-8fe7-9a7864fa77d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
0550553920315496032233342069149150189106...13114012311812012010813622056
1550554021315812152233342069149149220104...12714012211612011910813622071
25505541203158121522333420691491511413106...13114312511812212010813723292
3550554221316243792233342069149149895105...12913912411912012010813622760
4550554321316534072233342069149150268104...12914212211812012010813522128
..................................................................
9989756055332154601362272942069149150143104...12913912311711911810813562047
9989856055342154705942272942069149149142104...13114112411812011910813562120
9989956055352054705942272942069149150143105...13114112411812012010913662164
9990056055362155812442272942069149150143105...13013912311812012010813542030
9990156055372156869632272942069149150143104...13014112311712112010813762342
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99902 rows × 25 columns

\n", + "
" + ], + "text/plain": [ + " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 \\\n", + "0 5 505539 20 31549603 22333 42069 149 150 \n", + "1 5 505540 21 31581215 22333 42069 149 149 \n", + "2 5 505541 20 31581215 22333 42069 149 151 \n", + "3 5 505542 21 31624379 22333 42069 149 149 \n", + "4 5 505543 21 31653407 22333 42069 149 150 \n", + "... ... ... ... ... ... ... ... ... \n", + "99897 5 605533 21 5460136 22729 42069 149 150 \n", + "99898 5 605534 21 5470594 22729 42069 149 149 \n", + "99899 5 605535 20 5470594 22729 42069 149 150 \n", + "99900 5 605536 21 5581244 22729 42069 149 150 \n", + "99901 5 605537 21 5686963 22729 42069 149 150 \n", + "\n", + " Ch2 Ch3 ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax \\\n", + "0 189 106 ... 131 140 123 118 120 120 108 136 2 \n", + "1 220 104 ... 127 140 122 116 120 119 108 136 2 \n", + "2 1413 106 ... 131 143 125 118 122 120 108 137 2 \n", + "3 895 105 ... 129 139 124 119 120 120 108 136 2 \n", + "4 268 104 ... 129 142 122 118 120 120 108 135 2 \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "99897 143 104 ... 129 139 123 117 119 118 108 135 6 \n", + "99898 142 104 ... 131 141 124 118 120 119 108 135 6 \n", + "99899 143 105 ... 131 141 124 118 120 120 109 136 6 \n", + "99900 143 105 ... 130 139 123 118 120 120 108 135 4 \n", + "99901 143 104 ... 130 141 123 117 121 120 108 137 6 \n", + "\n", + " Summed \n", + "0 2056 \n", + "1 2071 \n", + "2 3292 \n", + "3 2760 \n", + "4 2128 \n", + "... ... \n", + "99897 2047 \n", + "99898 2120 \n", + "99899 2164 \n", + "99900 2030 \n", + "99901 2342 \n", + "\n", + "[99902 rows x 25 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "dfs = []\n", + "dfs.append(pd.read_csv('../data/260203/5_SiPM_ChannelsTest_Ch1-4_Ch2_QT_Thr20_Hys0_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260203/6_SiPM_ChannelsTest_Ch1-4_Ch6_QT_Thr20_Hys0_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260203/7_SiPM_ChannelsTest_Ch1-4_Ch10_QT_Thr20_Hys0_Background.csv'))\n", + "dfs.append(pd.read_csv('../data/260203/8_SiPM_ChannelsTest_Ch1-4_Ch14_QT_Thr20_Hys0_Background.csv'))\n", + "\n", + "dfs[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1f49e311-fe5b-4144-b076-94ef58c00cfd", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N_BINS = 512\n", + "BITS_12 = 2**12\n", + "\n", + "summed_spectra = [df['Summed'].tolist() for df in dfs]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3ed406a4-063e-4356-96b3-c1618bc5acfc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [64.392, 113.229, 82.507, 45.958]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(1800, 4000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Events rate ($s^{-1}$)')\n", + "# plt.xticks(np.arange(1800,4000,100))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "1d5d4034-c260-4380-9799-069c3dd46ccc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfs.append(pd.read_csv('../data/260203/9_SiPM_ChannelsTest_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv'))\n", + "summed_spectra.append(dfs[4]['Summed'])\n", + "times_ch14 = [times[3], 137.539]\n", + "legend = ['SiPM Ch.1-4', 'SiPM Ch.5-8']\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "for idx,s in enumerate([summed_spectra[3],summed_spectra[4]]):\n", + " plt.hist(s, N_BINS, range=(1800, 8000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.title('SIPHRA Ch14 active summed spectra, QT')\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Events rate ($s^{-1}$)')\n", + "# plt.xticks(np.arange(1800,4000,100))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4c130d5f-d182-4d99-b629-a3e03dfb1e66", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ch14_spectra = [dfs[3]['Ch14'], dfs[4]['Ch14']]\n", + "times_ch14 = [times[3], 137.539]\n", + "legend = ['SiPM Ch.1-4', 'SiPM Ch.5-8']\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "for idx,s in enumerate([ch14_spectra[0],ch14_spectra[1]]):\n", + " plt.hist(s, N_BINS, range=(0, 4500), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.title('SIPHRA Ch.14 active single-channel spectra, QT')\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Events rate ($s^{-1}$)')\n", + "# plt.xticks(np.arange(1800,4000,100))\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb new file mode 100644 index 0000000..dbabf7d --- /dev/null +++ b/notebooks/Untitled.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b2e685cc-738a-4b98-9983-3fb32e245a15", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a5e76308-c39b-410f-934c-8b40b81c8630", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9196704d-b0fc-4fc8-a350-9ae2cba0d208", + "metadata": {}, + "outputs": [], + "source": [ + "channels = [f'Ch{n}' for n in range(2,17,2)]\n", + "files_lsts = {}\n", + "files_lsts[channels[0]] = sorted(Path(project_root+'/data/260311').glob('SUBTRACTED*Ch2*.csv'))\n", + "files_lsts[channels[0]].pop(3)\n", + "files_lsts[channels[0]].pop(3)\n", + "files_lsts[channels[0]].pop(3)\n", + "for ch in channels[1:]:\n", + " files_lsts[ch] = sorted(Path(project_root+'/data/260311').glob(f'SUBTRACTED*{ch}*.csv'))\n", + "\n", + "vbiases = [28.0, 28.5, 29.0]\n", + "# Datasets\n", + "datasets = {}\n", + "for ch, lst in files_lsts.items():\n", + " datasets[ch] = []\n", + " datasets[ch].append(SiphraAcquisition(lst[2]))\n", + " datasets[ch].append(SiphraAcquisition(lst[0]))\n", + " datasets[ch].append(SiphraAcquisition(lst[1]))\n", + "del(files_lsts)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "540e967f-8cf8-4abb-9884-1c13966b45f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Ch2': [SIPHRAAcquisition(File: 'SUBTRACTED_17_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch2_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_01_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch2_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_09_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch2_Cs137')],\n", + " 'Ch4': [SIPHRAAcquisition(File: 'SUBTRACTED_18_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch4_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_02_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch4_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_10_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch4_Cs137')],\n", + " 'Ch6': [SIPHRAAcquisition(File: 'SUBTRACTED_19_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch6_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_03_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch6_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_11_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch6_Cs137')],\n", + " 'Ch8': [SIPHRAAcquisition(File: 'SUBTRACTED_20_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch8_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_04_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch8_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_12_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch8_Cs137')],\n", + " 'Ch10': [SIPHRAAcquisition(File: 'SUBTRACTED_21_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch10_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_05_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch10_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_13_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch10_Cs137')],\n", + " 'Ch12': [SIPHRAAcquisition(File: 'SUBTRACTED_22_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch12_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_06_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch12_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_14_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch12_Cs137')],\n", + " 'Ch14': [SIPHRAAcquisition(File: 'SUBTRACTED_23_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch14_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_07_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch14_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_15_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch14_Cs137')],\n", + " 'Ch16': [SIPHRAAcquisition(File: 'SUBTRACTED_24_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch16_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_08_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch16_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBTRACTED_16_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch16_Cs137')]}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "0cebeb17-52de-4f4c-981c-5409cdc95ca8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31.89442729949951" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets['Ch2'][0].exposure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e038aa17-88a8-4102-a198-7cd1e4412e96", + "metadata": {}, + "outputs": [], + "source": [ + "# Histograms\n", + "\n", + "for ch, dataset in datasets.items():\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9ceb181-d450-4ace-b064-996b01af676c", + "metadata": {}, + "outputs": [], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 2000, 1200)\n", + "cv.divide(4,2)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "hists = {}\n", + "legends = {}\n", + "\n", + "for idx, (ch, dataset) in enumerate(datasets.items()):\n", + " cv.cd(idx)\n", + " hists[ch] = []\n", + " lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", + " for acq, vbias in zip(dataset, vbiases):\n", + " hist = ROOT.TH1F(f\"{ch} Vbias={vbias}\", \"\", BITS12, 0, BITS12)\n", + " hist.Fill(acq[ch])\n", + " hist.Scale(1/acq.exposure)\n", + " hists[ch].append(hist)\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Counts\")\n", + " hist.SetLineColor(colors[idx])\n", + " del(hist)\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3ef60607-4f0e-4336-83d6-ab91f65b2189", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "cannot use 'list' as a set element (unhashable type: 'list')", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m ll = [(\u001b[33m'\u001b[39m\u001b[33ma\u001b[39m\u001b[33m'\u001b[39m,\u001b[32m1\u001b[39m), (\u001b[33m'\u001b[39m\u001b[33mb\u001b[39m\u001b[33m'\u001b[39m,\u001b[32m2\u001b[39m)]\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m ll_dict = {ll}\n", + "\u001b[31mTypeError\u001b[39m: cannot use 'list' as a set element (unhashable type: 'list')" + ] + } + ], + "source": [ + "ll = [('a',1), ('b',2)]\n", + "ll_dict = {ll}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Voltage_dependency.ipynb b/notebooks/Voltage_dependency.ipynb new file mode 100644 index 0000000..a5290db --- /dev/null +++ b/notebooks/Voltage_dependency.ipynb @@ -0,0 +1,695 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "248d5111-9526-4226-b6ef-a67dc0c71445", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c9421d1e-23fe-4585-905c-2f56603eafca", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "Cs237_MEV = 0.662" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1ebd8ed-1b60-42dd-b3d9-2372f7596aad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16']\n" + ] + } + ], + "source": [ + "#Make sure json files have matching names to the dat files. \n", + "\n", + "channels = [f'Ch{n}' for n in range(2,17,2)]\n", + "print(channels)\n", + "files_lsts = {}\n", + "files_lsts[channels[0]] = sorted(Path(project_root+'/data/260311').glob('SUBTRACTED*Ch2*.csv'))\n", + "files_lsts[channels[0]].pop(3)\n", + "files_lsts[channels[0]].pop(3)\n", + "files_lsts[channels[0]].pop(3)\n", + "for ch in channels[1:]:\n", + " files_lsts[ch] = sorted(Path(project_root+'/data/260311').glob(f'SUBTRACTED*{ch}*.csv'))\n", + "\n", + "vbiases = [28.0, 28.5, 29.0]\n", + "# Datasets\n", + "datasets = {}\n", + "for ch, lst in files_lsts.items():\n", + " datasets[ch] = []\n", + " datasets[ch].append(SiphraAcquisition(lst[2]))\n", + " datasets[ch].append(SiphraAcquisition(lst[0]))\n", + " datasets[ch].append(SiphraAcquisition(lst[1]))\n", + "\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kCyan, ROOT.kYellow, ROOT.kSpring, ROOT.kViolet]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4f82a8be-e799-4405-9464-bb085d20cdf3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
0520553921239503771882042069149150176104...13014212311712011310713522032
1520554021239589821882042069149150175103...1291411221181209710713522010
2520554120239589821882042069149150181103...12914012211712013310713622047
3520554221239778241882042069149149246103...12914012311712012910713622109
4520554320239778241882042069149149157103...129141122117121172107135142066
..................................................................
99959530553320352773941902242069149151236103...12714012311712011510713522086
99960530553420352773941902242069149150215104...13113912311812115010813622107
99961530553520352773941902242069149149266102...13014012311812114010713522141
99962530553620352773941902242069149151213104...13014012311811910410813722062
99963530553721353542541902242069149150203103...13014012411612013510813622073
\n", + "

99964 rows × 25 columns

\n", + "
" + ], + "text/plain": [ + " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 \\\n", + "0 5 205539 21 23950377 18820 42069 149 150 \n", + "1 5 205540 21 23958982 18820 42069 149 150 \n", + "2 5 205541 20 23958982 18820 42069 149 150 \n", + "3 5 205542 21 23977824 18820 42069 149 149 \n", + "4 5 205543 20 23977824 18820 42069 149 149 \n", + "... ... ... ... ... ... ... ... ... \n", + "99959 5 305533 20 35277394 19022 42069 149 151 \n", + "99960 5 305534 20 35277394 19022 42069 149 150 \n", + "99961 5 305535 20 35277394 19022 42069 149 149 \n", + "99962 5 305536 20 35277394 19022 42069 149 151 \n", + "99963 5 305537 21 35354254 19022 42069 149 150 \n", + "\n", + " Ch2 Ch3 ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax \\\n", + "0 176 104 ... 130 142 123 117 120 113 107 135 2 \n", + "1 175 103 ... 129 141 122 118 120 97 107 135 2 \n", + "2 181 103 ... 129 140 122 117 120 133 107 136 2 \n", + "3 246 103 ... 129 140 123 117 120 129 107 136 2 \n", + "4 157 103 ... 129 141 122 117 121 172 107 135 14 \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "99959 236 103 ... 127 140 123 117 120 115 107 135 2 \n", + "99960 215 104 ... 131 139 123 118 121 150 108 136 2 \n", + "99961 266 102 ... 130 140 123 118 121 140 107 135 2 \n", + "99962 213 104 ... 130 140 123 118 119 104 108 137 2 \n", + "99963 203 103 ... 130 140 124 116 120 135 108 136 2 \n", + "\n", + " Summed \n", + "0 2032 \n", + "1 2010 \n", + "2 2047 \n", + "3 2109 \n", + "4 2066 \n", + "... ... \n", + "99959 2086 \n", + "99960 2107 \n", + "99961 2141 \n", + "99962 2062 \n", + "99963 2073 \n", + "\n", + "[99964 rows x 25 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "dfs = []\n", + "dfs.append(pd.read_csv('../data/260203/4_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Banana.csv'))\n", + "dfs.append(pd.read_csv('../data/260203/3_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv'))\n", + "\n", + "dfs[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b86f145e-0b76-4aaa-8efb-2d2809a4a622", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N_BINS = 512\n", + "BITS_12 = 2**12\n", + "\n", + "summed_spectra = [df['Summed'].tolist() for df in dfs]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7b1167b3-85d2-4726-945f-29bc63241135", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [52.526, 50.871]\n", + "plt.figure(figsize=(10,10), dpi=80)\n", + "legend = ['Background', 'Banana']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(0, 10000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel(r'Energy channel')\n", + "plt.ylabel(r'Events rate ($s^{-1}$)')\n", + "plt.title('Ch.2 + Ch.14 Background and bBnana spectra')\n", + "# plt.xticks(np.arange(1800,4000,100))\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/baseline_subtraction.ipynb b/notebooks/baseline_subtraction.ipynb new file mode 100644 index 0000000..ced9272 --- /dev/null +++ b/notebooks/baseline_subtraction.ipynb @@ -0,0 +1,1143 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cda6a9c1-c4c0-4e69-b50d-7dcb1b8434f2", + "metadata": {}, + "source": [ + "# Performing baselie subtraction\n", + "\n", + "The baseline-subtracted histograms of an acquisition with 2 active channels were obtained. The baseline value for each channel is the mean of the readings from all the events triggered ONLY by external HOLD assertion. This value is subtracted from the values of each \"real\" event (triggered from internal HOLD).\n", + "\n", + "The dataset for this analysis is a background acquisition of 10 000 events. SIPHRA channels 2 and 14 were connected to SiPM channels 1-4 and 5-8, respectively, using charge comparator, with a threshold value of 20." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:32.276396Z", + "start_time": "2026-02-10T09:37:32.169808Z" + } + }, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# ROOT.gROOT.SetBatch(False)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", + "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7aa83758-b8f5-4a20-96c3-c8381ea71536", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "68a9cd08dc972f65", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:35.457562Z", + "start_time": "2026-02-10T09:37:33.669215Z" + } + }, + "outputs": [], + "source": [ + "# Datasets\n", + "acq_notSubtracted = SiphraAcquisition(project_root+'/data/260203/1_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv',\n", + " active_chs=[2,14],\n", + " exposure_sec=1)\n", + "acq_subtracted = SiphraAcquisition(project_root+'/data/260203/1_datdecodertest_subtractBaseline.csv',\n", + " active_chs=[2,14],\n", + " exposure_sec=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cae21a3ee684d69f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:43:25.401070Z", + "start_time": "2026-02-10T09:43:24.995723Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "SUMMED_XR=5000 # Right limit of the summed channel's x-axis\n", + "\n", + "c = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "names=['BL subt.', 'No correction']\n", + "hists = []\n", + "\n", + "lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "for idx, (acq, name) in enumerate(zip([acq_subtracted, acq_notSubtracted], names)):\n", + " # print(f\"Current file: {acq.filepath.name}\")\n", + " ch = 'Summed' #acq.active_chs[0]\n", + " hist = ROOT.TH1F(f\"{ch} {name}\", \"\", int(BITS9*SUMMED_XR/BITS12), 0, SUMMED_XR)\n", + " hist_singlech = ROOT.TH1F(f\"Ch. {acq.active_chs[0]} {name}\", \"\", BITS9, 0, BITS12)\n", + " hist.Fill(acq[ch]/len(acq.active_chs))\n", + " hist_singlech.Fill(acq[acq.active_chs[0]])\n", + " hist.Scale(1/acq.exposure)\n", + " hist_singlech.Scale(1/acq.exposure)\n", + "\n", + " hist_singlech2 = ROOT.TH1F(f\"Ch. {acq.active_chs[1]} {name}\", \"\", BITS9, 0, BITS12)\n", + " hist_singlech2.Fill(acq[acq.active_chs[1]])\n", + " hist_singlech2.Scale(1/acq.exposure)\n", + " #Preeting up..\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Counts\")\n", + " hist.SetLineColor(colors[idx])\n", + " hist.SetTitle(\"Baseline subtraction comparison\")\n", + " # hist.GetYaxis().SetRangeUser(0, 1e4)\n", + " lg.SetHeader(\"SIPHRA Channel\")\n", + " lg.AddEntry(hist, f\"{ch} {name}\", 'l')\n", + " hists.append(hist)\n", + " hists[-1].Draw('sames hist')\n", + " # hist_singlech.GetXaxis().SetTitle(\"ADC channel number\")\n", + " # hist_singlech.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist_singlech.SetLineColor(colors[idx + 2]+2)\n", + " hist_singlech2.SetLineColor(colors[idx + 4]+1)\n", + " # hist_singlech.SetTitle(\"Baseline subtraction comparison\")\n", + " lg.AddEntry(hist_singlech, f\"Ch. {acq.active_chs[0]} {name}\", 'l')\n", + " lg.AddEntry(hist_singlech2, f\"Ch. {acq.active_chs[1]} {name}\", 'l')\n", + " hists.append(hist_singlech)\n", + " hists[-1].Draw('sames hist')\n", + " hists.append(hist_singlech2)\n", + " hists[-1].Draw('sames hist')\n", + "c.SetLogy()\n", + "ROOT.gPad.Update()\n", + "for i, sp in enumerate(hists):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + "lg.Draw()\n", + "c.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4fe40546-908d-46cd-868c-f4c3e9bd0902", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv_4plots'):\n", + " ROOT.gROOT.FindObject('cv_4plots').Close()\n", + "\n", + "canvas4 = ROOT.TCanvas('cv_4plots', 'cv_4plots', 1600, 1200)\n", + "canvas4.Divide(2,2)\n", + "\n", + "legends = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "canvas4.cd(1)\n", + "hists[0].Draw('hist')\n", + "hists[3].Draw('sames hist')\n", + "legends[0].AddEntry(hists[0], \"Baseline subtracted\", 'l')\n", + "legends[0].AddEntry(hists[3], \"Not corrected\", 'l')\n", + "legends[0].SetHeader(\"\\'Summed\\' channel spectra\")\n", + "legends[0].Draw()\n", + "canvas4.cd(1).SetLogy()\n", + "\n", + "canvas4.cd(2)\n", + "hists[1].SetXTitle(\"ADC channel number\")\n", + "hists[1].SetYTitle(\"Counts\")\n", + "hists[1].Draw('hist')\n", + "hists[4].Draw('sames hist')\n", + "legends[1].AddEntry(hists[1], \"Baseline subtracted\", 'l')\n", + "legends[1].AddEntry(hists[4], \"Not corrected\", 'l')\n", + "legends[1].SetHeader(\"Ch. 2 spectra\")\n", + "legends[1].Draw()\n", + "canvas4.cd(2).SetLogy()\n", + "\n", + "canvas4.cd(3)\n", + "hists[2].SetXTitle(\"ADC channel number\")\n", + "hists[2].SetYTitle(\"Counts\")\n", + "hists[2].Draw('hist')\n", + "hists[5].Draw('sames hist')\n", + "legends[2].AddEntry(hists[2], \"Baseline subtracted\", 'l')\n", + "legends[2].AddEntry(hists[5], \"Not corrected\", 'l')\n", + "legends[2].SetHeader(\"Ch. 14 spectra\")\n", + "legends[2].Draw()\n", + "canvas4.cd(3).SetLogy()\n", + "\n", + "canvas4.cd(4)\n", + "h0_zoom = hists[0].Clone(\"hists_0_zoomed\")\n", + "h0_zoom.GetXaxis().SetRangeUser(0,BITS12)\n", + "h0_zoom.Draw('hist')\n", + "hists[1].Draw('sames hist')\n", + "hists[2].Draw('sames hist')\n", + "legends[3].AddEntry(h0_zoom, \"\\'Summed\\'\", 'l')\n", + "legends[3].AddEntry(hists[1], \"Ch. 2\", 'l')\n", + "legends[3].AddEntry(hists[2], \"Ch. 14\", 'l')\n", + "legends[3].SetHeader(\"Corrected channels\")\n", + "legends[3].Draw()\n", + "canvas4.cd(4).SetLogy()\n", + "\n", + "canvas4.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "46bd074b-d5aa-48a4-b027-f966314995cd", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADC channels range:\n", + "--------------------------------------------------\n", + "Channel\t\tBL-subtracted\tNo correction\n", + "--------------------------------------------------\n", + "Summed\t\t0.0 - 3965.0\t935.5 - 4880.5\n", + "Ch. 2\t\t0.0 - 3951.0\t51.0 - 4095.0\n", + "Ch. 14\t\t0.0 - 3979.0\t0.0 - 4095.0\n", + "\n", + "\n", + "\n", + "Number of values above single channel range in 'Summed' channel:\n", + "------------------------------\n", + "BL-subtracted\tNo correction\n", + "------------------------------\n", + " 0 \t 22 \n" + ] + } + ], + "source": [ + "print(\"ADC channels range:\\n\"\n", + " f\"{\"\":->50}\\n\"\n", + " \"Channel\\t\\tBL-subtracted\\tNo correction\\n\"\n", + " f\"{\"\":->50}\\n\"\n", + " f\"Summed\\t\\t{acq_subtracted['s'].min()/2} - {acq_subtracted['s'].max()/2}\\t{acq_notSubtracted['s'].min()/2} - {acq_notSubtracted['s'].max()/2}\\n\"\n", + " f\"Ch. 2\\t\\t{acq_subtracted[2].min()} - {acq_subtracted[2].max()}\\t{acq_notSubtracted[2].min()} - {acq_notSubtracted[2].max()}\\n\"\n", + " f\"Ch. 14\\t\\t{acq_subtracted[14].min()} - {acq_subtracted[14].max()}\\t{acq_notSubtracted[14].min()} - {acq_notSubtracted[14].max()}\\n\")\n", + "\n", + "print(\"\\n\\n\"\n", + " \"Number of values above single channel range in \\'Summed\\' channel:\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " \"BL-subtracted\\tNo correction\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " f\"{len(acq_subtracted['s'][acq_subtracted['s'][:]/2>BITS12]):^13}\\t{len(acq_notSubtracted['s'][acq_notSubtracted['s'][:]/2>BITS12]):^13}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae86c5f0-762f-4cca-a8bb-b0c00694959e", + "metadata": {}, + "source": [ + "The baseline subtraction is performed directly at the conversion script. No averaging over the number of channels is carried out by the conversion script, but the above spectra and data correspond to values averaged over the number of active channels (2).\n", + "\n", + "The values of the baselines of all the channels are between 98 and 150.\n", + "\n", + "The range of the summed channel does not exceed the maximum single-channel value (4096) after baseline correction.\n", + "\n", + "**Questions:**\n", + "- Is this baseline correction enough for pedestal subtraction?\n", + "- What about the large peak at the lower range of the spectrum? Is it still originating from noise at this stage?" + ] + }, + { + "cell_type": "markdown", + "id": "19798694-0ef8-43fb-a09c-6b87cc8b03f5", + "metadata": {}, + "source": [ + "# Calibration of corrected spectrum with the backgrond $^{40}$K and $^{208}$Tl peaks\n", + "\n", + "The current process of calibration to actual energies is shown here. The two peaks of $^{40}$K and $^{208}$Tl present in the background are used for this purpose. The emission lines of these nuclides are located at 1.460 MeV and 2.614 MeV, respectively. First, both peaks are fitted to a Gaussian with exponentially-decaying background in the baseline-subtracted background histogram. The Gaussian means are taken as the actual locations of the emission lines. The raw data is linearly transformed to aligh with these two points.\n", + "\n", + "To verify the calibration, we use the same calibration parameters to fit a spectrum taken in the pressence of a sample of $^{137}$Cs. After background subtraction, it is expected that the mean of the Gaussian fit of the resulting peak, lands at 0.661MeV." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a4f39db9-ad0d-4f5b-b3b5-ea67040593a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Datasets\n", + "folder = Path(project_root)/'data/260209'\n", + "times_lst = [0, 119.634, 161.062, 148.920, 164.828, 34.836, 80.792, 54.386, 90.837, 24.645, 24.144, 24.606, 23.381, 24.720, 24.916, 24.848, 24.934] # Index coincide with starting number of file name\n", + "\n", + "# Select SiPM channels 5-8, SIPHRA channel 14 -> gives cleaner signal\n", + "# Background is file with #4, source is file with #16, with preffix 'BLsubtr_'\n", + "BGIDX = 4; SRCIDX=16; ACTIVECH=14\n", + "bgfile = sorted(folder.glob(f'BLsubtr_{BGIDX}*.csv'))[0]\n", + "srcfile = sorted(folder.glob(f'BLsubtr_{SRCIDX}*.csv'))[0]\n", + "\n", + "acq_bg = SiphraAcquisition(bgfile, active_chs=ACTIVECH, exposure_sec=times_lst[BGIDX])\n", + "acq_src = SiphraAcquisition(srcfile, active_chs=ACTIVECH, exposure_sec=times_lst[SRCIDX])\n", + "colors_clb = [colors[1]-1, colors[3]-1, colors[0]-2] # colors for BG, signal and clean, respectively\n", + "NORMFACTOR = acq_src.exposure/(acq_bg.exposure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0b44ed9c-6f45-4174-8f81-1407bcdacc05", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADC channels range after baseline subtraction:\n", + "CHANNEL 14\n", + "------------------------------\n", + "Data set\tRange\n", + "------------------------------\n", + "Background\t0.0 - 3924.0\n", + "Source\t\t0.0 - 3797.0\n", + "\n", + "SUMMED CHANNEL\n", + "------------------------------\n", + "Data set\tRange\n", + "------------------------------\n", + "Background\t0.0 - 3924.0\n", + "Source\t\t0.0 - 3797.0\n", + "\n" + ] + } + ], + "source": [ + "print(\"ADC channels range after baseline subtraction:\\n\"\n", + " \"CHANNEL 14\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " \"Data set\\tRange\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " f\"Background\\t{acq_bg[ACTIVECH].min()} - {acq_bg[ACTIVECH].max()}\\n\"\n", + " f\"Source\\t\\t{acq_src[ACTIVECH].min()} - {acq_src[ACTIVECH].max()}\\n\\n\"\n", + " \"SUMMED CHANNEL\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " \"Data set\\tRange\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " f\"Background\\t{acq_bg['s'].min()} - {acq_bg['s'].max()}\\n\"\n", + " f\"Source\\t\\t{acq_src['s'].min()} - {acq_src['s'].max()}\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "cddfadfd-d1a7-4257-9712-15c6daf6d565", + "metadata": {}, + "source": [ + "Baseline subtraction is effectively filtering out noise from inactive channels. It is noteworthy that, when subtracting single channels, the upper ADC channels will be empty, hence there is a loss of dynamic range due to baseline subtraction." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2093b302-1f12-4e19-86f6-74506d4e5ff6", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: Background (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Signal 137Cs (Potential memory leak).\n" + ] + } + ], + "source": [ + "# Raw histograms\n", + "hist_bg = ROOT.TH1F(\"Background\", \"\", BITS9, 0, BITS12)\n", + "hist_bg.Fill(acq_bg[ACTIVECH])\n", + "hist_bg.Scale(NORMFACTOR) # Normalize counts to the source exposure time\n", + "\n", + "hist_src = ROOT.TH1F(\"Signal 137Cs\", \"\", BITS9, 0, BITS12)\n", + "hist_src.Fill(acq_src[ACTIVECH])\n", + "\n", + "# Bacground subtraction\n", + "hist_clean = hist_src.Clone(\"137Cs no BG\")\n", + "hist_clean.Add(hist_bg, -1)\n", + "# Removing negative bins\n", + "for i in range(1, hist_clean.GetNbinsX() + 1): # bin 0 is the underflow\n", + " if hist_clean.GetBinContent(i) < 0: hist_clean.SetBinContent(i, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "eacf6495-684b-42fa-9eab-8ca73fb2afef", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in : Cannot set Y axis to log scale\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_bg.GetXaxis().SetTitle(\"ADC Channel\")\n", + "hist_bg.GetYaxis().SetTitle(\"Normalized counts\")\n", + "hist_bg.SetLineColor(colors_clb[0])\n", + "hist_bg.SetFillColor(colors_clb[0])\n", + "hist_src.SetLineColor(colors_clb[1])\n", + "hist_src.SetFillColorAlpha(colors_clb[1], 0.8)\n", + "lg1.AddEntry(hist_bg, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_bg.Draw(\"hist\")\n", + "hist_src.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "hist_bg.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean.GetXaxis().SetTitle(\"ADC Channel\")\n", + "hist_clean.GetYaxis().SetTitle(\"Normalized counts\")\n", + "hist_clean.SetLineColor(colors_clb[2])\n", + "hist_clean.SetFillColor(colors_clb[2])\n", + "hist_clean.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "1b94d6b2-ea7d-41ea-acb2-a7e820e9dd34", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 3.07992\n", + "Chi2 = 6.15985\n", + "NDf = 45\n", + "Edm = 4.33544e-06\n", + "NCalls = 378\n", + "Const = 967.676 +/- 315.198 \n", + "Denom = 204.714 +/- 16.8531 \n", + "Norm = 26.0236 +/- 2.92104 \n", + "Mean = 748.998 +/- 10.0065 \n", + "Sigma = 69.1169 +/- 10.4622 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 4.22284\n", + "Chi2 = 8.44567\n", + "NDf = 45\n", + "Edm = 1.92878e-06\n", + "NCalls = 607\n", + "Const = 65.7641 +/- 57.5374 \n", + "Denom = 441.486 +/- 153.51 \n", + "Norm = 1.79752 +/- 0.994549 \n", + "Mean = 1240.31 +/- 25.9607 \n", + "Sigma = 44.4995 +/- 28.325 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Deleting canvas with same name: cv_fit\n", + "Error in : Cannot set Y axis to log scale\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fit background peaks\n", + "xrange_k40 = (600,1000)\n", + "nrm_k40_i = 55\n", + "mean_k40_i = 740\n", + "xrange_tl208 = (1000,1400)\n", + "nrm_tl208_i = 7\n", + "mean_tl208_i = 1210\n", + "hist_bg_fit = hist_bg.Clone()\n", + "hist_bg_fit.GetYaxis().SetRangeUser(0,1e3)\n", + "hist_bg.SetTitle(r\"^{40}K and ^{208}Tl peaks fitting\")\n", + "fit_fn_k40, fit_res_k40 = fit_peak_expbg(hist_bg_fit, name=\"K40_peak\", xl=xrange_k40[0], xr=xrange_k40[1], norm=nrm_k40_i, mean=mean_k40_i, showFit=True)\n", + "fit_fn_tl208, fit_res_tl208 = fit_peak_expbg(hist_bg_fit, name=\"Tl208_peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm_tl208_i, mean=mean_tl208_i, showFit=True)\n", + "\n", + "cv_fit = ROOT.TCanvas(\"cv_fit\", \"cv_fit\", 800,600)\n", + "hist_bg_fit.SetFillStyle(0)\n", + "hist_bg_fit.Draw(\"hist\")\n", + "cv_fit.SetLogy()\n", + "cv_fit.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "3e0906bf-c280-4c5e-b555-871735d01a54", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: Calibrated background (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Calibrated signal (Potential memory leak).\n" + ] + } + ], + "source": [ + "# Calibrate data and get calibrated histograms\n", + "K40FIT = fit_fn_k40.GetParameter('Mean')\n", + "TL208FIT = fit_fn_tl208.GetParameter('Mean')\n", + "clb_slope = (TL208_MEV - K40_MEV)/(TL208FIT - K40FIT)\n", + "clb_const = K40_MEV - clb_slope * K40FIT\n", + "\n", + "data_bg_clb = acq_bg[ACTIVECH]*clb_slope + clb_const\n", + "data_src_clb = acq_src[ACTIVECH]*clb_slope + clb_const\n", + "\n", + "CLBHISTS_XR = np.max([data_bg_clb.max(), data_src_clb.max()])\n", + "\n", + "hist_bg_clb = ROOT.TH1F(\"Calibrated background\", \"Calibrated Histograms\", BITS9, 0, CLBHISTS_XR)\n", + "hist_src_clb = ROOT.TH1F(\"Calibrated signal\", \"Calibrated Histograms\", BITS9, 0, CLBHISTS_XR)\n", + "\n", + "hist_bg_clb.Fill(data_bg_clb)\n", + "hist_bg_clb.Scale(NORMFACTOR)\n", + "\n", + "hist_src_clb.Fill(data_src_clb)\n", + "\n", + "hist_clean_clb = hist_src_clb.Clone(\"Calibrated signal no BG\")\n", + "hist_clean_clb.Add(hist_bg_clb, -1)\n", + "\n", + "for hist, clr in zip([hist_bg_clb, hist_src_clb, hist_clean_clb], colors_clb):\n", + " hist.GetXaxis().SetTitle(\"Energy (MeV)\")\n", + " hist.GetYaxis().SetTitle(\"Normalized counts\")\n", + " hist.SetLineColor(clr)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "5416087a-578e-4d42-8157-2516db3c6b39", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in : Cannot set Y axis to log scale\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_bg_clb.SetFillColor(colors_clb[0])\n", + "hist_src_clb.SetFillColorAlpha(colors_clb[1], 0.8)\n", + "lg1.AddEntry(hist_bg_clb, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_bg_clb.Draw(\"hist\")\n", + "hist_src_clb.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean_clb.SetFillColor(colors_clb[2])\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "dcbad448-c208-4054-a4ab-e2b71b7ed2a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 5628.78\n", + "Chi2 = 11257.6\n", + "NDf = 112\n", + "Edm = 1.88824e-07\n", + "NCalls = 124\n", + "Constant = 4002.29 +/- 17.4275 \n", + "Mean = 0.479418 +/- 0.000489701 \n", + "Sigma = 0.136089 +/- 0.000348374 \t (limited)\n" + ] + } + ], + "source": [ + "res = hist_clean_clb.Fit(\"gaus\", \"L S\", \"\", 0, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "6d7f6090-75e8-4779-8a1c-35f1cad0a43d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error in the location of Cs peak: 27.5%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Deleting canvas with same name: cv_fit\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cv_fit = ROOT.TCanvas(\"cv_fit\", \"cv_fit\", 800,600)\n", + "# hist_bg_fit.SetFillStyle(0)\n", + "hist_clean_clb.Draw(\"hist\")\n", + "hist_clean_clb.SetFillColor(colors_clb[2]-6)\n", + "cv_fit.SetLogy()\n", + "cv_fit.Draw()\n", + "\n", + "\n", + "err = (CS137_MEV - hist_clean_clb.GetFunction(\"gaus\").GetParameter(\"Mean\"))/CS137_MEV\n", + "print(f\"Error in the location of Cs peak: {err*100:.1f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "58f9c3b4-03c0-453a-9d37-cd9cd565ad5f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FWHM=0.32 Energy resolution at 480MeV: 66.84%\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "sigma = 0.136089\n", + "FWHM = np.sqrt(8*np.log(2))*sigma\n", + "print(f\"{FWHM=:.2f} Energy resolution at 480MeV: {FWHM*100/0.479418:.2f}%\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/energyResolution.ipynb b/notebooks/energyResolution.ipynb new file mode 100644 index 0000000..3656e54 --- /dev/null +++ b/notebooks/energyResolution.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4a3b1bc5-ec73-4d69-a840-07d057f4e2eb", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition, MetadataLoader\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = [1.460]\n", + "TL208_MEV = [2.614]\n", + "CS137_MEV = [0.661]\n", + "ANNIHIL_MEV = 0.511\n", + "NA22_MEV = [ANNIHIL_MEV, 1.2745, 1.2745+ANNIHIL_MEV]\n", + "CO60_MEV = [1.1732, 1.3325, 1.1732+1.3325]\n", + "\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75493f51-2089-4f0a-8057-0f2c2bf7466f", + "metadata": {}, + "outputs": [], + "source": [ + "files = sorted((project_root/'data/260323').glob('SUBT_*'))\n", + "acqs = [SiphraAcquisition(file) for file in files]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5ee51f11-cd24-44c1-bfe5-f0e3b7d8a85c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SIPHRAAcquisition(File: 'SUBT_01_EnergyResolution_thr30_gain1over100_1over3_Background'),\n", + " SIPHRAAcquisition(File: 'SUBT_02_EnergyResolution_thr30_gain1over100_1over3_Cs137'),\n", + " SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'),\n", + " SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22'),\n", + " SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'),\n", + " SIPHRAAcquisition(File: 'SUBT_06_EnergyResolution_thr30_gain1over100_1over3_Co60'),\n", + " SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'),\n", + " SIPHRAAcquisition(File: 'SUBT_08_EnergyResolution_thr30_gain1over100_1over3_Am241'),\n", + " SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "88f4c856-78c0-4ca6-ad58-cc10f3b53649", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cs-137\n", + "Na-22\n", + "Co-60\n", + "Am-241\n", + "{'Cs-137': , 'Cs-137_BG': , 'Cs-137_clean': , 'Na-22': , 'Na-22_BG': , 'Na-22_clean': , 'Co-60': , 'Co-60_BG': , 'Co-60_clean': , 'Am-241': , 'Am-241_BG': , 'Am-241_clean': }\n" + ] + } + ], + "source": [ + "# histograms\n", + "hists = {}\n", + "sources = []\n", + "for sgnl, bg in zip(acqs[1::2], acqs[2::2]):\n", + " filepath = sgnl.filepath.stem\n", + " src = (MetadataLoader.load(sgnl.metadataFile)).source\n", + " sources.append(src)\n", + " print(src)\n", + " # Signal and Background\n", + " hist_sgnl = ROOT.TH1F(f\"{src} Signal\", \"\", BITS12, 0, BITS12)\n", + " hist_bg = ROOT.TH1F(f\"{src} Background\", \"\", BITS12, 0 , BITS12)\n", + " hist_sgnl.Fill(sgnl['s']/len(sgnl.active_chs))\n", + " hist_bg.Fill(bg['s']/len(bg.active_chs))\n", + " hist_bg.Scale(sgnl.exposure/bg.exposure)\n", + " # Clean spectrum\n", + " hist_clean = hist_sgnl.Clone(f\"{src} Clean\")\n", + " hist_clean.Add(hist_bg, -1)\n", + " for hist in [hist_sgnl, hist_bg, hist_clean]:\n", + " # hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Normalized counts\")\n", + " hists[src] = hist_sgnl\n", + " hists[f\"{src}_BG\"] = hist_bg\n", + " hists[f\"{src}_clean\"] = hist_clean\n", + " del hist_sgnl, hist_bg\n", + "print(hists)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36f654a6-bd64-471f-a0e3-01927db52e3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", + "cv.Divide(2,2)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", + " cv.cd(idx+1)\n", + " \n", + " sgnl = hists[src]\n", + " bg = hists[src+'_BG']\n", + " clean = hists[src+'_clean']\n", + " \n", + " lg.AddEntry(sgnl, \"Signal\")\n", + " lg.AddEntry(bg, \"Background\")\n", + " lg.AddEntry(clean, \"Subtracted\")\n", + " sgnl.SetLineColor(colors[0])\n", + " bg.SetLineColor(colors[1])\n", + " clean.SetLineColor(colors[2])\n", + " sgnl.SetTitle(src)\n", + " sgnl.Draw(\"hist\")\n", + " bg.Draw(\"sames hist\")\n", + " clean.Draw(\"sames hist\")\n", + " ROOT.gPad.Update()\n", + " for i, sp in enumerate([sgnl, bg, clean]):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " lg.Draw()\n", + " cv.cd(idx+1).SetLogy()\n", + " cv.Draw()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6db7ba78-ddd9-4d76-8bba-bf310f8f1221", + "metadata": {}, + "outputs": [], + "source": [ + "fit_xrange = {\n", + " 'Cs-137': [(100, 270)],\n", + " 'Na-22': [(325, 408), (436,508)],\n", + " 'Co-60': [(200, 361), (419, 520)],\n", + " 'Am-241': [(8, 30)]\n", + "}\n", + "\n", + "# Fit Cs-137\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6b5a5060-fe8f-48ba-af47-c12bca5ea672", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'),\n", + " SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'),\n", + " SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'),\n", + " SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs[2::2]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/etcTests/converters_explore.ipynb b/notebooks/etcTests/converters_explore.ipynb new file mode 100644 index 0000000..0518f1f --- /dev/null +++ b/notebooks/etcTests/converters_explore.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T12:58:03.008075Z", + "start_time": "2026-02-10T12:58:03.004553Z" + }, + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "from pathlib import Path\n", + "\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "66f88d66e88d9206", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:03:05.467415Z", + "start_time": "2026-02-10T13:03:05.464159Z" + } + }, + "outputs": [], + "source": [ + "pr = (Path(os.getcwd())/'..').resolve()\n", + "current_file = os.path.join(os.getcwd(),'converters_explore.ipynb')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3f48f1abf97a9445", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T12:59:11.369712Z", + "start_time": "2026-02-10T12:59:11.364233Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pr.resolve()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ffb7e7ed30a2834e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:01:53.512177Z", + "start_time": "2026-02-10T13:01:53.507397Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'.ipynb'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p, n = os.path.split(current_file)\n", + "b, e = os.path.splitext(n)\n", + "e" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "85d7ca84600ea5fd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:21:21.966397Z", + "start_time": "2026-02-10T13:21:21.964395Z" + } + }, + "outputs": [], + "source": [ + "cf = pr/'etcTests/converters_explore.ipynb'" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2238be3ec8b03b0c", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:21:23.712944Z", + "start_time": "2026-02-10T13:21:23.708626Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/etcTests/converters_explore.ipynb')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cf" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a92d7f060b8cce59", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:21:25.777143Z", + "start_time": "2026-02-10T13:21:25.772082Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cf.is_file()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9d4a881e48223aa5", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:30:35.025902Z", + "start_time": "2026-02-10T13:30:35.022787Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "for i, e in enumerate([1,2,3,4,5]):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "cd2d503e805701b8", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T13:45:03.620720Z", + "start_time": "2026-02-10T13:45:03.615591Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/etcTests')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cf.parent" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "739b3aae440091c0", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T14:29:35.397990Z", + "start_time": "2026-02-10T14:29:35.395620Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260204.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260205.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260206.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260209.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/Untitled.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/Untitled1.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/Untitled2.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/banana.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/histogram_summed.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/spectra_current_thrsh.ipynb'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/spectrum_plotter.ipynb')]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(pr.glob(\"*.ipynb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3d79d86bf3a004d3", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T14:40:11.754644Z", + "start_time": "2026-02-10T14:40:10.738660Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 99.06it/s]\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "from time import sleep\n", + "\n", + "with tqdm(total=100) as pbar:\n", + " for i in range(10):\n", + " sleep(0.1)\n", + " pbar.update(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "534dadb0-f4ae-48a5-b33f-228b3c072d17", + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/etcTests/siphraAcq_test_json.ipynb b/notebooks/etcTests/siphraAcq_test_json.ipynb new file mode 100644 index 0000000..3a0a219 --- /dev/null +++ b/notebooks/etcTests/siphraAcq_test_json.ipynb @@ -0,0 +1,125 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "083ff4be-8adb-4b9f-955a-bb7c0e8633c8", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "afade2b8-2a73-4b6b-ac90-b000e5928d24", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e4fb3482-e47a-44a1-b36f-f4a89dd908b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Datasets \n", + "\n", + "acq1 = SiphraAcquisition(project_root+'/data/260225/SB_1_SiPM_Ch1-4_Ch2_Ch5_8_Ch14_Background.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2532c4ac-3d89-48be-85c4-947e18b92c5b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'filepath': PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260225/SB_1_SiPM_Ch1-4_Ch2_Ch5_8_Ch14_Background.csv'),\n", + " 'metadataFile': PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260225/1_SiPM_Ch1-4_Ch2_Ch5_8_Ch14_Background.json'),\n", + " 'active_chs': [2, 14],\n", + " 'exposure': 129.73262667655945,\n", + " 'sipm_chs': ['Ch1-4 to Ch2', 'Ch5-8 to Ch14'],\n", + " 'n_events': 250000,\n", + " 'name': None}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars(acq1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5ace8361-b67e-4a94-baf7-f40e7658529d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2: array([200., 109., 125., ..., 55., 49., 59.], shape=(249910,)),\n", + " 14: array([151., 33., 117., ..., 1., 0., 8.], shape=(249910,))}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acq1['a']" + ] + } + ], + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/gainTesting.ipynb b/notebooks/gainTesting.ipynb new file mode 100644 index 0000000..7aa8f5b --- /dev/null +++ b/notebooks/gainTesting.ipynb @@ -0,0 +1,687 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 66, + "id": "cc81fa7c-d43e-4c17-854d-8bc51e7bdf91", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "f14c60fa-8e5a-4c1b-bd05-6d8f0a23851d", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "9d972c1c-3934-4a10-9e7c-4e7b9caa4b3f", + "metadata": {}, + "outputs": [], + "source": [ + "files1 = sorted((project_root/'data/260317').glob('1*.csv'))\n", + "acqs1 = [SiphraAcquisition(file) for file in files1]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "2696fa6e-8416-4df7-9bf9-62d00adcee3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SIPHRAAcquisition(File: '10_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '11_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '12_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '15_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '16_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '17_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1'),\n", + " SIPHRAAcquisition(File: '18_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1')]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs1.pop(3)\n", + "acqs1.pop(3)\n", + "acqs1" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "af88b5d2-3d88-4310-ad81-90986efb6ffd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'10'" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acqs1[0].filepath.stem[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "8d9cd696-eb5e-46c8-9673-73e2afc2ebc5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: cmis_gain 1/10 (Potential memory leak).\n", + "Warning in : Replacing existing TH1: cmis_gain 1/100 (Potential memory leak).\n", + "Warning in : Replacing existing TH1: cmis_gain 1/200 (Potential memory leak).\n", + "Warning in : Replacing existing TH1: cmis_gain 1/400 (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "lg.SetHeader('CMIS gain')\n", + "\n", + "ci_gain='1V/0.75nC'\n", + "cmis_gains=['1/10', '1/100', '1/200', '1/400']\n", + "hists={}\n", + "\n", + "for idx, (acq, cmsg) in enumerate(zip(acqs1[:4], cmis_gains)):\n", + " hist = ROOT.TH1F(f\"cmis_gain {cmsg}\", f\"{cmsg}\", BITS12, 0, BITS12)\n", + " hist.Fill(acq['s']/len(acq.active_chs))\n", + " hist.Scale(1/acq.exposure)\n", + " # Preeting up ...\n", + " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist.SetLineColor(colors[idx]+2)\n", + " # legend and storing\n", + " lg.AddEntry(hist, f\"{cmsg}\")\n", + " hists[f\"Acq{acq.filepath.stem[:2]}\"] = hist\n", + " del hist\n", + " hists[f\"Acq{acq.filepath.stem[:2]}\"].Draw('sames hist')\n", + "cv.SetLogy()\n", + "lg.Draw()\n", + "cv.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "65e8172d-a2bc-4306-87b7-66027afc15f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibration with acquisition 15(Signal) and 16 (BG)\n", + "files2 = sorted([p for p in (project_root/'data/260317').iterdir() if p.stem[11:13] in ['15', '16']])\n", + "acqs2 = [SiphraAcquisition(file) for file in files2]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "b831a930-27c1-442f-8dd0-e30f2d8a84a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260317/SUBTRACTED_15_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1.csv'),\n", + " PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260317/SUBTRACTED_16_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1.csv')]" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files2" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0fcebaeb-0ace-4f57-b151-3a80a51535e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(acqs2[0].active_chs)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "f6a83f88-b203-4af0-9f0b-18a7b5a0338f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposures:\n", + " Signal:\t220.216 s\n", + " Background:\t625.392 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: Signal (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Background (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "# lg.SetHeader('')\n", + "\n", + "ci_gain='1V/0.75nC'\n", + "labels=['Signal', 'Background']\n", + "\n", + "ref_exposure = acqs2[0].exposure\n", + "\n", + "for idx, (acq, label) in enumerate(zip(acqs2, labels)):\n", + " hist = ROOT.TH1F(label, label, BITS12, 0, BITS12)\n", + " hist.Fill(acq['s']/len(acq.active_chs))\n", + " # hist.Scale(ref_exposure/acq.exposure)\n", + " # Preeting up ...\n", + " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC, CMIS gain = 1/400\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Counts\")\n", + " hist.SetLineColor(colors[idx]+2)\n", + " # legend and storing\n", + " lg.AddEntry(hist, label)\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"] = hist\n", + " del hist\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"].Draw('sames hist')\n", + "\n", + "# hist_subt = hists['AcqSUBTRACTED_15'].Clone('Subtracted')\n", + "# hist_subt.Add(hists['AcqSUBTRACTED_16'], -1)\n", + "# hist_subt.Draw('sames hist')\n", + "# hist_subt.SetLineColor(colors[2])\n", + "# lg.AddEntry(hist_subt, 'Subtracted')\n", + "\n", + "cv.SetLogy()\n", + "lg.Draw()\n", + "cv.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "print(f\"Exposures:\\n Signal:\\t{acqs2[0].exposure:>.3f} s\\n Background:\\t{acqs2[1].exposure:>.3f} s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "05919cfa-b075-4898-8f7b-56039d4c8da1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Acq10': ,\n", + " 'Acq11': ,\n", + " 'Acq12': ,\n", + " 'Acq15': ,\n", + " 'AcqSUBTRACTED_10': ,\n", + " 'AcqSUBTRACTED_11': ,\n", + " 'AcqSUBTRACTED_16': ,\n", + " 'AcqSUBTRACTED_15': }" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hists" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "8e7a42e8-c3a4-451c-b635-5d37c1167faf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 7605.91\n", + "Chi2 = 15211.8\n", + "NDf = 197\n", + "Edm = 1.10191e-06\n", + "NCalls = 138\n", + "Constant = 1267.88 +/- 5.7826 \n", + "Mean = 172.076 +/- 0.0854468 \n", + "Sigma = 22.9615 +/- 0.0616869 \t (limited)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "files2 = sorted((project_root/'data/260317').glob('SUBTRACTED_*'))\n", + "acqs2 = [SiphraAcquisition(file) for file in files2]\n", + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "\n", + "hist = hist_subt.Clone('hist_subt_calib')\n", + "hist.SetTitle(r'^{22}Na subtracted spectrum, CI gain = 1/0.75 pC, CMIS gain = 1/400')\n", + "fit1 = hist.Fit('gaus', 'L S', \"\", 100, 300)\n", + "hist.Draw()\n", + "cv.SetLogy()\n", + "cv.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "daf8a1de-c392-4879-a4a9-619db60f669e", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibration with acquisition 15(Signal) and 16 (BG)\n", + "files2 = sorted((project_root/'data/260317').glob('SUBTRACTED_*'))\n", + "acqs2 = [SiphraAcquisition(file) for file in files2]\n", + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", + "# lg.SetHeader('CMIS gain')\n", + "\n", + "ci_gain='1V/0.75nC'\n", + "labels=['Signal', 'Background']\n", + "hists={}\n", + "\n", + "for idx, (acq, label) in enumerate(zip(acqs2, labels)):\n", + " hist = ROOT.TH1F(label, label, BITS12, 0, BITS12)\n", + " hist.Fill(acq['s']/len(acq.active_chs))\n", + " hist.Scale(1/acq.exposure)\n", + " # Preeting up ...\n", + " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC, CMIS gain = 1/400\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist.SetLineColor(colors[idx]+2)\n", + " # legend and storing\n", + " lg.AddEntry(hist, label)\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"] = hist\n", + " del hist\n", + " hists[f\"Acq{acq.filepath.stem[:13]}\"].Draw('sames hist')\n", + "\n", + "\n", + "\n", + "cv.SetLogy()\n", + "lg.Draw()\n", + "cv.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "2028d39f-0e60-4bbe-b3c9-55cbbc63b14b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2004" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2278 - 274" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "f3576c1a-50d1-4778-920f-1dd754dd473d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1204" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1841 - 637" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "a74af979-2cdc-4da3-8164-59ab0a3be28d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "560" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "984-424" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/histogram_summed.ipynb b/notebooks/histogram_summed.ipynb new file mode 100644 index 0000000..7c025d8 --- /dev/null +++ b/notebooks/histogram_summed.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d476cf44-4356-49a8-890b-c65fbd833335", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "218b623e-3fa9-4146-b944-a9df401a72e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
058221386028829642069149150293103...12914012411812027810813522309
158321400280829642069149150223103...129140123117120236106136142192
258420400280829642069149150254104...12914012311812119010913622189
358520400280829642069149149307105...128140122119120325108135142372
458621445339629642069149151308105...13014012311712028310813722332
\n", + "

5 rows × 25 columns

\n", + "
" + ], + "text/plain": [ + " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 Ch2 Ch3 \\\n", + "0 5 82 21 3860288 296 42069 149 150 293 103 \n", + "1 5 83 21 4002808 296 42069 149 150 223 103 \n", + "2 5 84 20 4002808 296 42069 149 150 254 104 \n", + "3 5 85 20 4002808 296 42069 149 149 307 105 \n", + "4 5 86 21 4453396 296 42069 149 151 308 105 \n", + "\n", + " ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax Summed \n", + "0 ... 129 140 124 118 120 278 108 135 2 2309 \n", + "1 ... 129 140 123 117 120 236 106 136 14 2192 \n", + "2 ... 129 140 123 118 121 190 109 136 2 2189 \n", + "3 ... 128 140 122 119 120 325 108 135 14 2372 \n", + "4 ... 130 140 123 117 120 283 108 137 2 2332 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv('../data/260123/3_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Background_attempt2.28.5V.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6e72aec1-8f58-4b31-9ee7-058b813041ad", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N_BINS = 512\n", + "BITS_12 = 2**12\n", + "\n", + "summed_spectrum = df['Summed'].tolist()\n", + "plt.figure(figsize=(14,10), dpi=80)\n", + "plt.hist(summed_spectrum, N_BINS, range=(0, 4*BITS_12), log=True, histtype='step')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1c42a169-327e-42ed-a3c4-97a7390bbe36", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4096" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2**12" + ] + } + ], + "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", + 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"source": [ + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e45c25aa-ef8a-412e-87d1-d106e57a3424", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
0560000121595446384242069149151614106...13014112411812152711013722893
1560000221610680384242069149150621104...131140125118122689110137143062
2560000321620254384242069149150482105...13014112211812037210813622597
3560000421630512384242069149151476104...13014112311812040510913722629
4560000521638502384242069149150799106...12914112511812155110913723104
..................................................................
9967356999952010745499428742069149151915106...131142124118122981109137143657
9967456999962111026960428742069149151900105...13114012411912166610813823320
99675569999720110269604287420691491521074105...13114212511912195711013723790
9967656999982011026960428742069149150975106...13014112311912169810913723421
9967756999992111598563428742069149149935104...12914012411812072710813523403
\n", + "

99678 rows × 25 columns

\n", + "
" + ], + "text/plain": [ + " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 \\\n", + "0 5 600001 21 595446 3842 42069 149 151 \n", + "1 5 600002 21 610680 3842 42069 149 150 \n", + "2 5 600003 21 620254 3842 42069 149 150 \n", + "3 5 600004 21 630512 3842 42069 149 151 \n", + "4 5 600005 21 638502 3842 42069 149 150 \n", + "... ... ... ... ... ... ... ... ... \n", + "99673 5 699995 20 10745499 4287 42069 149 151 \n", + "99674 5 699996 21 11026960 4287 42069 149 151 \n", + "99675 5 699997 20 11026960 4287 42069 149 152 \n", + "99676 5 699998 20 11026960 4287 42069 149 150 \n", + "99677 5 699999 21 11598563 4287 42069 149 149 \n", + "\n", + " Ch2 Ch3 ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax \\\n", + "0 614 106 ... 130 141 124 118 121 527 110 137 2 \n", + "1 621 104 ... 131 140 125 118 122 689 110 137 14 \n", + "2 482 105 ... 130 141 122 118 120 372 108 136 2 \n", + "3 476 104 ... 130 141 123 118 120 405 109 137 2 \n", + "4 799 106 ... 129 141 125 118 121 551 109 137 2 \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "99673 915 106 ... 131 142 124 118 122 981 109 137 14 \n", + "99674 900 105 ... 131 140 124 119 121 666 108 138 2 \n", + "99675 1074 105 ... 131 142 125 119 121 957 110 137 2 \n", + "99676 975 106 ... 130 141 123 119 121 698 109 137 2 \n", + "99677 935 104 ... 129 140 124 118 120 727 108 135 2 \n", + "\n", + " Summed \n", + "0 2893 \n", + "1 3062 \n", + "2 2597 \n", + "3 2629 \n", + "4 3104 \n", + "... ... \n", + "99673 3657 \n", + "99674 3320 \n", + "99675 3790 \n", + "99676 3421 \n", + "99677 3403 \n", + "\n", + "[99678 rows x 25 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "dfs = []\n", + "dfs.append(pd.read_csv('../data/260127/6_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold10.csv'))\n", + "dfs.append(pd.read_csv('../data/260127/7_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold20.csv'))\n", + "dfs.append(pd.read_csv('../data/260127/8_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold5.csv'))\n", + "dfs.append(pd.read_csv('../data/260127/9_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold15.csv'))\n", + "\n", + "dfs[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d8be8d5a-6d8e-4666-bace-5874e4a1c711", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N_BINS = 512\n", + "BITS_12 = 2**12\n", + "\n", + "summed_spectra = [df['Summed'].tolist() for df in dfs]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "71a3799e-2638-4d63-ae76-86d7ff643f35", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14,14), dpi=80)\n", + "legend = ['Thrsh = 10', 'Thrsh = 20', 'Thrsh = 5', 'Thrsh = 15']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(0, 4*BITS_12), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel('Channel')\n", + "plt.ylabel('Number of events')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dd891565-4321-4120-b738-8a31ccd64ed4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [31.708, 893.006, 24.152, 335.703]\n", + "plt.figure(figsize=(14,10), dpi=80)\n", + "legend = ['Thrsh = 10', 'Thrsh = 20', 'Thrsh = 5', 'Thrsh = 15']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(0, 4*BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel('Channel')\n", + "plt.ylabel('Rate of events')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6cd2bea1-15c6-4863-b29a-2fd86584959d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "times = [31.708, 893.006, 24.152, 335.703]\n", + "plt.figure(figsize=(14,10), dpi=80)\n", + "legend = ['Thrsh = 10', 'Thrsh = 20', 'Thrsh = 5', 'Thrsh = 15']\n", + "for idx,s in enumerate(summed_spectra):\n", + " plt.hist(s, N_BINS, range=(2000, 10000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", + "plt.legend()\n", + "plt.xlabel('Channel')\n", + "plt.ylabel('Rate of events')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bcebd34-62ae-4fb7-aa41-23df1379432a", + "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": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/spectrum_plotter.ipynb b/notebooks/spectrum_plotter.ipynb new file mode 100644 index 0000000..fdd784b --- /dev/null +++ b/notebooks/spectrum_plotter.ipynb @@ -0,0 +1,1022 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:32.276396Z", + "start_time": "2026-02-10T09:37:32.169808Z" + } + }, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", + "sys.path.append(project_root)\n", + "\n", + "# ROOT.gROOT.SetBatch(False)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", + "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "44c9a6d0-0e35-49fd-af28-5fa39de26322", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "180/5/2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "68a9cd08dc972f65", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:35.457562Z", + "start_time": "2026-02-10T09:37:33.669215Z" + } + }, + "outputs": [], + "source": [ + "BITS_12 = 2**12\n", + "N_BINS = 512\n", + "\n", + "dfs_SiPM58_BG = []\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/1_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "times_SiPM58_BG = [119.634, 161.062, 148.920, 164.828]\n", + "\n", + "dfs_SiPM14_BG = []\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", + "times_SiPM14_BG = [34.836, 80.792, 54.386, 90.837]\n", + "\n", + "dfs_SiPM14_Cs137 = []\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/12_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/11_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/10_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/9_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "times_SiPM14_Cs137 = [23.381, 24.606, 24.144, 24.645]\n", + "\n", + "dfs_SiPM58_Cs137 = []\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/13_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/14_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/15_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/16_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", + "times_SiPM58_Cs137 = [24.720, 24.916, 24.848, 24.934]\n", + "\n", + "SIPHRA_Ch_list = ['Ch2', 'Ch6', 'Ch10', 'Ch14']\n", + "dfs = [dfs_SiPM14_BG, dfs_SiPM14_Cs137, dfs_SiPM58_BG, dfs_SiPM58_Cs137]\n", + "data_SiPM_chs = ['1-4', '1-4', '5-8', '5-8']\n", + "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cae21a3ee684d69f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:43:25.401070Z", + "start_time": "2026-02-10T09:43:24.995723Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: stats Title: A special TPaveText to draw histogram statistics.\n", + "Name: stats Title: A special TPaveText to draw histogram statistics.\n", + "Name: stats Title: A special TPaveText to draw histogram statistics.\n", + "Name: stats Title: A special TPaveText to draw histogram statistics.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject(\"c1\"):\n", + " ROOT.gROOT.FindObject(\"c1\").Close()\n", + "\n", + "c1 = ROOT.TCanvas(\"c1\", \"c1\", 800, 600)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.15)\n", + "\n", + "IamLegend = ROOT.TLegend(0.65, 0.7, 0.8, 0.88)\n", + "\n", + "ch_spectra = []\n", + "frame_lst = dfs_SiPM14_BG\n", + "colors = [ROOT.kBlue, ROOT.kRed, ROOT.kGreen, ROOT.kOrange]\n", + "for df, ch, lg, t, c in zip(frame_lst, SIPHRA_Ch_list, legend, times_SiPM14_BG, colors):\n", + " hist = ROOT.TH1F(ch, \"Background spectra SiPM Ch. 1-4\", N_BINS, 0, BITS_12)\n", + " for dp in df[ch]:\n", + " hist.Fill(dp)\n", + " hist.Scale(1/t)\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist.SetLineColor(c-2)\n", + " st=hist.FindObject(\"stats\")\n", + " # st.SetX1NDC(0.65)\n", + " IamLegend.SetHeader('SIPHRA channel')\n", + " IamLegend.AddEntry(hist, lg, 'l')\n", + " ch_spectra.append(hist)\n", + "\n", + "ch_spectra[0].Draw(\"hist\")\n", + "for sp in ch_spectra[1:]:\n", + " sp.Draw(\"hist,sames\")\n", + "c1.SetLogy()\n", + "IamLegend.Draw()\n", + "\n", + "ROOT.gPad.Update()\n", + "Yinit = 0.82\n", + "for i, sp in enumerate(ch_spectra):\n", + " st = sp.FindObject(\"stats\")\n", + " print(st)\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " \n", + "\n", + "c1.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b9c56acf-bfdb-4895-aa41-7f8c6b7ff2ac", + "metadata": {}, + "outputs": [], + "source": [ + "# def gauspeak(n_init):\n", + "# return f\"[{n_init}]*TMath::Gaus(x, [{n_init+1}], [{n_init+2}]\"\n", + "\n", + "# formula = \"[0]*TMath::Exp(-x/[1]) + [2]*TMath::Gaus(x, [3], [4])\"\n", + "\n", + "# c2 = ROOT.TCanvas(\"c2\", \"c2\", 800, 600)\n", + "# # bg_exp = ROOT.TF1(\"ExpDecayBG\", \"[p0]*TMath::Exp(x*[p1]/[p2])\", 0, BITS_12)\n", + "# # gaussian1 = ROOT.TF1(\"K40peak\", \"TMath::Gaus(x, [mean], [sigma])\", 800, 1100)\n", + "# # gaussian2 = ROOT.TF1(\"Tl208peak\", \"TMath::Gaus(x, [mean], [sigma])\", 1300, 1600)\n", + "\n", + "# bg = ROOT.TF1(\"ExpDecayBG\", formula, 700, 1200, 5)\n", + "# bg.SetParNames(\"exp_fact\", \"exp_denom\", \"norm\", \"mean\", \"sigma\")\n", + "# bg.SetParameters(180, 200, 4, 904, 4)\n", + "\n", + "# fitResult = ch_spectra[2].Fit(bg, \"L S\", \"\", 700, 1200)\n", + "# ch_spectra[2].GetXaxis().SetRangeUser(0, BITS_12)\n", + "# ch_spectra[2].Draw(\"SAME hist\")\n", + "# c2.SetLogy()\n", + "# c2.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e8495329-2fcb-47eb-bb43-ad3f00010c74", + "metadata": {}, + "outputs": [], + "source": [ + "# def gauspeak(n_init):\n", + "# return f\"[{n_init}]*TMath::Gaus(x, [{n_init+1}], [{n_init+2}])\"\n", + "\n", + "# def bg_exp(n_init):\n", + "# return f\"[{n_init}]*TMath::Exp(-x/[{n_init+1}])\"\n", + "# norm = bg.GetParameter(2)\n", + "# mean = bg.GetParameter(3)\n", + "# sigma = bg.GetParameter(4)\n", + "# const = bg.GetParameter(0)\n", + "# expdenom = bg.GetParameter(1)\n", + "# c3 = ROOT.TCanvas(\"c3\", \"c3\", 800, 600)\n", + "# peak = ROOT.TF1(\"K40_peak\", gauspeak(0), 400, 1500, 3)\n", + "# peak.SetParameters(norm, mean, sigma)\n", + "# peak.SetLineColor(ROOT.kMagenta)\n", + "# onlybg = ROOT.TF1(\"onlybg\", \"[0]*TMath::Exp(-x/[1])\", 100, 2000, 2)\n", + "# onlybg.SetParameters(const, expdenom)\n", + "# onlybg.SetLineColor(ROOT.kBlue)\n", + "# onlybg.SetLineStyle(2)\n", + "# ch_spectra[2].Draw(\"SAME hist\")\n", + "# peak.Draw(\"same\")\n", + "# onlybg.Draw(\"same\")\n", + "# c3.SetLogy()\n", + "# c3.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f7ae8560-be45-46f7-b37f-aa423cd53cc7", + "metadata": {}, + "outputs": [], + "source": [ + "def gauspeak(n_init):\n", + " return f\"[{n_init}]*TMath::Gaus(x, [{n_init+1}], [{n_init+2}])\"\n", + "\n", + "def bg_exp(n_init):\n", + " return f\"[{n_init}]*TMath::Exp(-x/[{n_init+1}])\"\n", + "\n", + "def peak_and_bg():\n", + " return f\"{bg_exp(0)}+{gauspeak(2)}\"\n", + "\n", + "def fitpeak(hsit, name, xl, xr, norm, mean, sigma=70, const=200, denom=200, showFit=False, keep_prev_fncs=True):\n", + " fit_fn = ROOT.TF1(name, peak_and_bg(), xl, xr, 5)\n", + " fit_fn.SetParNames(\"Const\", \"Denom\", \"Norm\", \"Mean\", \"Sigma\")\n", + " fit_fn.SetParameters(const, denom, norm, mean, sigma)\n", + " options = \"L S\" if showFit else \"0 S\"\n", + " options += \" +\" if keep_prev_fncs else \"\"\n", + " fit_result = hist.Fit(fit_fn, options, \"\", xl, xr)\n", + " return fit_fn, fit_result\n", + "\n", + "\n", + "# fit_fn_ch14, fit_ch14 = fitpeak(ch_spectra[3], name=\"K40_peak\", xl=650, xr=1200, norm=4, mean=840)\n", + "\n", + "# c4 = ROOT.TCanvas(\"c4\", \"c4\", 800, 600)\n", + "# ch_spectra[3].GetXaxis().SetRangeUser(0, BITS_12)\n", + "# # ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", + "# ch_spectra[3].Draw(\"hist\")\n", + "# c4.SetLogy()\n", + "# c4.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "77b328fc-c345-46fa-a780-8354a292c5e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.866612\n", + "Chi2 = 1.73322\n", + "NDf = 64\n", + "Edm = 1.48724e-06\n", + "NCalls = 1442\n", + "Const = 22.546 +/- 26.3142 \n", + "Denom = 234.686 +/- 86.4285 \n", + "Norm = 0.634481 +/- 0.336032 \n", + "Mean = 894.211 +/- 40.4785 \n", + "Sigma = -68.8513 +/- 58.3939 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.573121\n", + "Chi2 = 1.14624\n", + "NDf = 64\n", + "Edm = 4.3817e-07\n", + "NCalls = 204\n", + "Const = 120.222 +/- 75.8598 \n", + "Denom = 209.018 +/- 33.8978 \n", + "Norm = 2.09451 +/- 0.638154 \n", + "Mean = 858.191 +/- 28.3061 \n", + "Sigma = 73.0178 +/- 29.7389 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.778431\n", + "Chi2 = 1.55686\n", + "NDf = 64\n", + "Edm = 1.3971e-07\n", + "NCalls = 191\n", + "Const = 178.751 +/- 108.57 \n", + "Denom = 205.251 +/- 35.1254 \n", + "Norm = 2.78572 +/- 0.7156 \n", + "Mean = 888.101 +/- 28.8723 \n", + "Sigma = 85.669 +/- 31.3128 \n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "MinFCN = 0.486844\n", + "Chi2 = 0.973688\n", + "NDf = 64\n", + "Edm = 1.03434e-06\n", + "NCalls = 224\n", + "Const = 93.493 +/- 63.0911 \n", + "Denom = 216.779 +/- 38.4718 \n", + "Norm = 1.98405 +/- 0.605315 \n", + "Mean = 859.999 +/- 29.857 \n", + "Sigma = 75.4189 +/- 30.6475 \n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tl208peak_init = 1380\n", + "xrange_tl208 = (1100, 1800)\n", + "norms_init = [0.1, 0.4, 0.4, 0.5]\n", + "sig_init = 70\n", + "\n", + "fit_fns = []\n", + "tl208peaks_means = []\n", + "tl208peaks_sigmas = []\n", + "\n", + "for hist, nrm, ch in zip(ch_spectra, norms_init, legend):\n", + " fit_fn, fit_res = fitpeak(hist, name=\"Tl208peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm, mean=tl208peak_init, showFit=True)\n", + " fit_fns.append(fit_fn)\n", + " tl208peaks_means.append(fit_fns[-1].GetParameter(3))\n", + " tl208peaks_sigmas.append(fit_fns[-1].GetParameter(4))\n", + "\n", + "c5 = ROOT.TCanvas(\"c5\", \"c5\", 800, 600)\n", + "# ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", + "\n", + "for sp in ch_spectra:\n", + " sp.Draw(\"hist,sames\")\n", + "c5.SetLogy()\n", + "c5.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5669d26f-4957-487c-bbd7-6c2f43b9a298", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Deleting canvas with same name: c5\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c5 = ROOT.TCanvas(\"c5\", \"c5\", 800, 600)\n", + "# ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", + "t = []\n", + "for sp in ch_spectra:\n", + " sp.Draw(\"hist,sames\")\n", + " tl208_fn = sp.GetFunction(\"Tl208peak\")\n", + " text_x = tl208_fn.GetParameter(3)\n", + " text_y = tl208_fn.Eval(text_x)\n", + " t.append(ROOT.TText(text_x, text_y,f\"{text_x:.2f}\"))\n", + " t[-1].SetTextSize(10)\n", + " t[-1].SetTextAngle(90)\n", + " t[-1].Draw()\n", + " # sp.GetFunction(\"Tl208peak\").Delete()\n", + "for sp in ch_spectra:\n", + " k40_fn = sp.GetFunction(\"K40_peak\")\n", + " text_x = k40_fn.GetParameter(3)\n", + " text_y = k40_fn.Eval(text_x)\n", + " t.append(ROOT.TText(text_x, text_y,f\"{text_x:.2f}\"))\n", + " t[-1].SetTextSize(10)\n", + " t[-1].SetTextAngle(90)\n", + " t[-1].Draw()\n", + "\n", + "IamLegend.Draw()\n", + "c5.SetLogy()\n", + "c5.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f85ab3b2-072e-4d81-b68f-96534fbe5129", + "metadata": {}, + "outputs": [], + "source": [ + "#def get_peak_from_fit(fit_fn, " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cd49a392-2942-4c17-8741-aae09b231840", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Calibration: E = 0.0023 * Ch + -0.5498\n", + "\n", + "Max. Energy: 9.02 MeV\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "\n", + "raw_data = frame_lst[3][SIPHRA_Ch_list[3]].to_numpy()\n", + "\n", + "calib_m = (TL208_MEV - K40_MEV) / (tl208peaks_means[3] - k40peaks_means[3])\n", + "calib_b = K40_MEV - calib_m*k40peaks_means[3]\n", + "print(f\"\\nCalibration: E = {calib_m:.4f} * Ch + {calib_b:.4f}\\n\")\n", + "\n", + "calib_data = (raw_data * calib_m + calib_b)\n", + "print(f\"Max. Energy: {BITS_12*calib_m+calib_b:.2f} MeV\")\n", + "\n", + "hist_calib = ROOT.TH1F(\"Ch14_BG_calib\", \"Spectrum calibrated w.r.t. K40 & Tl208\", N_BINS, min(calib_data), max(calib_data))\n", + "\n", + "hist_calib.Fill(calib_data)\n", + "hist_calib.Scale(1/times_SiPM14_BG[3])\n", + "hist_calib.SetLineColor(colors[3]-3)\n", + "\n", + "c = ROOT.TCanvas(\"c\", \"c\", 800, 600)\n", + "hist_calib.Draw(\"hist\")\n", + "c.SetLogy()\n", + "c.Draw()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a104c522-b356-4966-9ef4-6175f5c94926", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + " Invalid FitResult (status = 2 )\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 0\n", + "NDf = 0\n", + "Edm = 1.06209e-16\n", + "NCalls = 89\n", + "Const = 200 +/- 0 \n", + "Denom = 0.1 +/- 0 \n", + "Norm = -103.981 +/- 0 \n", + "Mean = 0.66 +/- 0 \n", + "Sigma = 0.5 +/- 0 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Abnormal termination of minimization.\n", + "Warning in : Deleting canvas with same name: c\n", + "Error in : Cannot set Y axis to log scale\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "source_raw_dat = dfs_SiPM14_Cs137[3][SIPHRA_Ch_list[3]].to_numpy()\n", + "source_calib_data = source_raw_dat*calib_m + calib_b\n", + "source_hist_calib = ROOT.TH1F(\"Ch14_Cs137_calib\", \"\", N_BINS, min(source_calib_data), max(source_calib_data))\n", + "source_hist_calib.Fill(source_calib_data)\n", + "source_hist_calib.Scale(1/times_SiPM14_Cs137[3])\n", + "\n", + "fitpeak(source_hist_calib, name=\"Cs137peak\", xl=0.2, xr=1.2, norm=170, mean=0.66, sigma=0.5, const=200, denom=0.1, showFit=True)\n", + "\n", + "hist_calib.GetYaxis().SetRangeUser(0,2e2)\n", + "\n", + "c = ROOT.TCanvas(\"c\", \"c\", 800, 600)\n", + "source_hist_calib.Draw(\"hist same\")\n", + "source_hist_calib.GetXaxis().SetTitle(\"Energy (MeV)\")\n", + "source_hist_calib.GetYaxis().SetTitle(r\"Count Rate (s^{-1})\")\n", + "hist_calib.Draw(\"hist same\")\n", + "hist_calib.SetFillColorAlpha(colors[3]-3, 0.5)\n", + "source_hist_calib.SetFillColor(ROOT.kBlue)\n", + "\n", + "c.SetLogy()\n", + "c.Draw()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb5c869d-50a5-4acc-a2c3-d26e9b61ee59", + "metadata": {}, + "outputs": [], + "source": [ + "fit_fn, fit_res = fitpeak(hist, name=\"Tl208peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm, mean=tl208peak_init, showFit=True)\n", + " fit_fns.append(fit_fn)\n", + " tl208peaks_means.append(fit_fns[-1].GetParameter(3))\n", + " tl208peaks_sigmas.append(fit_fns[-1].GetParameter(4))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ROOT in Conda)", + "language": "python", + "name": "comcube" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/siphractrl/ComptonCam_DMA_To_RAW_File/Makefile b/siphractrl/ComptonCam_DMA_To_RAW_File/Makefile new file mode 100644 index 0000000..300b533 --- /dev/null +++ b/siphractrl/ComptonCam_DMA_To_RAW_File/Makefile @@ -0,0 +1,17 @@ +# Read DMA datas and write them in a file + +CFLAGS = -g -Wall -Wextra -Wpedantic -pedantic -lhiredis + +MAIN = dma_to_raw_file + +all: $(MAIN) + +$(MAIN): src/dma_to_raw_file.c + $(CC) $^ -o $@ $(CFLAGS) + +clean: + find . -name "*.o" -delete + find . -name "*~" -delete + +distclean: clean + $(RM) -rf $(MAIN) diff --git a/siphractrl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c b/siphractrl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c new file mode 100644 index 0000000..96c7319 --- /dev/null +++ b/siphractrl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c @@ -0,0 +1,437 @@ +/** + * @file dma_to_raw_file.c + * + * @date 17 September 2020 + * @version 0.1 + * @brief Read DMA datas and write them in a file + */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "/home/ubuntu/repositories/KM_comptoncamdmas/files/comptoncamdmas.h" + +typedef struct { + bool debug; + bool toggle; + bool blocking; + bool verbose; + bool redis; + char redis_prefix[256]; + redisContext *redis_context; + + char input_device_name[256]; + int input_device_handler; + + bool output_file_1_enable; + char output_file_1_name[256]; + FILE *output_file_1_handler; + + bool output_file_2_enable; + char output_file_2_name[256]; + FILE *output_file_2_handler; + + uint32_t max_block_size; + uint32_t max_count; + + bool max_file_size_enable; + uint32_t max_file_size; + uint32_t file_name_id; + + uint32_t* data; + + char prefix_filename[256]; +} ConfigDMAToRaw, *pConfigDMAToRaw; + +int configure_s2mm (int file_desc) +{ + int ret_val; + uint32_t reg0a = 0xaa; + uint32_t args_for_configure_s2mm[2]; + args_for_configure_s2mm[0] = (uint32_t) 0; + args_for_configure_s2mm[1] = (uint32_t) ®0a; + + ret_val = ioctl(file_desc, CONFIGURE_S2MM, &args_for_configure_s2mm[0]); + + return ret_val; +} + +int manage_command_line (pConfigDMAToRaw pConfig, int argc, char * const* argv){ + int opt; + + while((opt = getopt(argc, argv, ":i:o:k:c:s:df:tbvr:")) != -1) { + switch(opt) { + case 'i': + printf("input_device_name: %s\n", optarg); + snprintf(pConfig->input_device_name, sizeof pConfig->input_device_name, "%s", optarg); + break; + + case 'o': + printf("output_file_1_name: %s\n", optarg); + snprintf(pConfig->output_file_1_name, sizeof pConfig->output_file_1_name, "%s", optarg); + pConfig->output_file_1_enable = true; + break; + + case 'k': + printf("output_file_2_name: %s\n", optarg); + snprintf(pConfig->output_file_2_name, sizeof pConfig->output_file_2_name, "%s", optarg); + pConfig->output_file_2_enable = true; + break; + + case 'c': + printf("max_count: %s\n", optarg); + pConfig->max_count = atoi (optarg); + break; + + case 's': + printf("max_block_size: %s\n", optarg); + pConfig->max_block_size = atoi (optarg); + break; + + case 'd': + printf("option debug ON\n"); + pConfig->debug = true; + break; + + case 'f': + printf("max_file_size: %s\n", optarg); + pConfig->max_file_size = atoi (optarg); + pConfig->max_file_size_enable = true; + break; + + case 'b': + printf("WARN !!! option blocking read ON\n"); + pConfig->blocking = true; + break; + + case 't': + printf("WARN !!! toggle debug in kernel driver !!!!!!!!!!!\n"); + pConfig->toggle = true; + break; + + case 'v': + printf("verbose mode : ON\n"); + pConfig->verbose = true; + break; + + case 'r': + printf ("REDIS : ON\n"); + snprintf(pConfig->redis_prefix, sizeof pConfig->redis_prefix, "%s", optarg); + pConfig->redis = true; + break; + + case ':': + printf("option needs a value\n"); + break; + + case '?': + printf("unknown option: %c\n", optopt); + break; + } + } + return 0; +} + +int set_a_redis_ex_string (pConfigDMAToRaw pConfig, char * prefix, char * register_name, char * ex, char * value) { + //char tmp_rediscommand[300]; + redisReply *reply; + //snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "SET %s%s %s %s", prefix, register_name, ex, value); + reply = (redisReply *)redisCommand(pConfig->redis_context, "SET %s%s %s %s", prefix, register_name, ex, value); + printf("SET: %s\n", reply->str); + freeReplyObject(reply); + return 0; +} + +int set_a_redis_uint32_t (pConfigDMAToRaw pConfig, char * prefix, char * register_name, char * ex, uint32_t value) { + //char tmp_rediscommand[300]; + redisReply *reply; + //snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "SET %s%s %s %lu", prefix, register_name, ex, (unsigned long) value); + reply = (redisReply *)redisCommand(pConfig->redis_context, "SET %s%s %s %lu", prefix, register_name, ex, (unsigned long) value); + printf("SET: %s\n", reply->str); + freeReplyObject(reply); + return 0; +} + +int get_a_redis_string (pConfigDMAToRaw pConfig, char * prefix, char * register_name, char * result) { + char tmp_rediscommand[300]; + redisReply *reply; + snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "GET %s%s", prefix, register_name); + reply = (redisReply *)redisCommand(pConfig->redis_context, tmp_rediscommand); + printf("*Redis command (%s) -> server returned %s\n", tmp_rediscommand, reply->str); + strcpy(result, reply->str); + freeReplyObject(reply); + return 0; +} + +int get_a_redis_uint32_t (pConfigDMAToRaw pConfig, char * prefix, char * register_name, uint32_t * result) { + char tmp_rediscommand[300]; + redisReply *reply; + snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "GET %s%s", prefix, register_name); + reply = (redisReply *)redisCommand(pConfig->redis_context, tmp_rediscommand); + printf("*Redis command (%s) -> server returned %s\n", tmp_rediscommand, reply->str); + *result = atoi (reply->str); + freeReplyObject(reply); + return 0; +} + +int get_a_redis_bool (pConfigDMAToRaw pConfig, char * prefix, char * register_name, bool * result) { + char tmp_rediscommand[300]; + redisReply *reply; + snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "GET %s%s", prefix, register_name); + reply = (redisReply *)redisCommand(pConfig->redis_context, tmp_rediscommand); + printf("*Redis command (%s) -> server returned %s\n", tmp_rediscommand, reply->str); + *result = atoi (reply->str); + freeReplyObject(reply); + return 0; +} + +int configure_redis_context (pConfigDMAToRaw pConfig) { + setbuf(stdout, NULL); + setbuf(stderr, NULL); + // Open up a connection to Redis + char host[] = "127.0.0.1"; + int port = 6379; + + pConfig->redis_context = redisConnect(host, port); + if (pConfig->redis_context != NULL && pConfig->redis_context->err) { + printf("Error: %sn", pConfig->redis_context->errstr); + return -1; + } + else { + printf("Redis server connected at %s:%d\n", host, port); + } + // Ping Redis server + redisReply *reply; + reply = (redisReply *)redisCommand(pConfig->redis_context,"PING"); + printf("Redis server PING returned %s (%s)\n", reply->str, (!(strcmp(reply->str, "PONG")) ? "SUCCESS" : "FAIL")); + freeReplyObject(reply); + return 0; +} + +int manage_redis (pConfigDMAToRaw pConfig) { + char output_dir_1[256]; + char output_dir_2[256]; + + if (configure_redis_context (pConfig) < 0) {return -1;} + + get_a_redis_bool (pConfig, pConfig->redis_prefix, "_DMA_BLOCKING_MODE", &pConfig->blocking); + get_a_redis_string (pConfig, pConfig->redis_prefix, "_DMA_INPUT", pConfig->input_device_name); + get_a_redis_uint32_t (pConfig, pConfig->redis_prefix, "_DMA_MAX_FILE_SIZE", &pConfig->max_file_size); + if (pConfig->max_file_size > 0) { + pConfig->max_file_size_enable = true; + } + get_a_redis_uint32_t (pConfig, pConfig->redis_prefix, "_DMA_MAX_BLOCK_SIZE", &pConfig->max_block_size); + get_a_redis_string (pConfig, "", "FSW_LOG_PREFIX", pConfig->prefix_filename); + get_a_redis_string (pConfig, "", "DMA_OUTPUT_DIRECTORY_1", output_dir_1); + get_a_redis_string (pConfig, "", "DMA_OUTPUT_DIRECTORY_2", output_dir_2); + get_a_redis_bool (pConfig, "", "DMA_ENABLE_OUTPUT_DIRECTORY_1", &pConfig->output_file_1_enable); + get_a_redis_bool (pConfig, "", "DMA_ENABLE_OUTPUT_DIRECTORY_2", &pConfig->output_file_2_enable); + + snprintf(pConfig->output_file_1_name, sizeof pConfig->output_file_1_name, "%s%s_%s", output_dir_1, pConfig->prefix_filename, pConfig->redis_prefix); + snprintf(pConfig->output_file_2_name, sizeof pConfig->output_file_2_name, "%s%s_%s", output_dir_2, pConfig->prefix_filename, pConfig->redis_prefix); + + return 0; +} + +int open_input_device (pConfigDMAToRaw pConfig) { + pConfig->input_device_handler = open(pConfig->input_device_name, 0); + if (pConfig->input_device_handler < 0) { + printf ("Can't open device file: %s\n", pConfig->input_device_name); + return -1; + } + else { + printf ("Device file opened : %s\n", pConfig->input_device_name); + } + return 0; +} + +int open_new_outputs (pConfigDMAToRaw pConfig) { + char tmp_output_file_name[268]; + + pConfig->file_name_id += 1; + + if (pConfig->output_file_1_enable) { + if (pConfig->output_file_1_handler != NULL) { + fclose (pConfig->output_file_1_handler); + } + snprintf(tmp_output_file_name, sizeof tmp_output_file_name, "%s.%04d", pConfig->output_file_1_name, pConfig->file_name_id); + pConfig->output_file_1_handler = fopen(tmp_output_file_name, "a"); + if (pConfig->output_file_1_handler == NULL) { + printf ("Can't open output file: %s\n", tmp_output_file_name); + pConfig->output_file_1_enable = false; + } + else { + printf ("Output file 1 opened : %s\n", tmp_output_file_name); + } + } + + if (pConfig->output_file_2_enable) { + if (pConfig->output_file_2_handler != NULL) { + fclose (pConfig->output_file_2_handler); + } + snprintf(tmp_output_file_name, sizeof tmp_output_file_name, "%s.%04d", pConfig->output_file_2_name, pConfig->file_name_id); + pConfig->output_file_2_handler = fopen(tmp_output_file_name, "a"); + if (pConfig->output_file_2_handler == NULL) { + printf ("Can't open output file: %s\n", tmp_output_file_name); + pConfig->output_file_2_enable = false; + } + else { + printf ("Output file 2 opened : %s\n", tmp_output_file_name); + } + } + if (!(pConfig->output_file_1_enable | pConfig->output_file_2_enable)) { + return -1; + } + return 0; +} + +int close_all (pConfigDMAToRaw pConfig) { + free (pConfig->data); + if (pConfig->output_file_1_enable) { + fclose (pConfig->output_file_1_handler); + } + if (pConfig->output_file_2_enable) { + fclose (pConfig->output_file_2_handler); + } + close (pConfig->input_device_handler); + + return 0; +} + +int Init_DMA (pConfigDMAToRaw pConfig) { + + uint32_t args_for_ioctl[2]; + uint32_t size_to_write = 0; + pConfig->data = (uint32_t*) calloc ((pConfig->max_block_size >> 2), sizeof (uint32_t)); + args_for_ioctl[0] = (uint32_t) &size_to_write; + args_for_ioctl[1] = (uint32_t) pConfig->data; + + if (pConfig->toggle) + { + ioctl(pConfig->input_device_handler, TOGGLE_DEBUG, &args_for_ioctl[0]); + } + + configure_s2mm (pConfig->input_device_handler); + + return 0; +} + +int Read_DMA_And_Write_File (pConfigDMAToRaw pConfig) { + struct timeval tv1, tv2; + uint32_t size_to_write = 0; + uint32_t size_writen = 0; + uint32_t counter = 0; + uint32_t idx; + + uint32_t args_for_ioctl[2]; + args_for_ioctl[0] = (uint32_t) &size_to_write; + args_for_ioctl[1] = (uint32_t) pConfig->data; + + + do { + gettimeofday (&tv1, NULL); + + size_to_write = pConfig->max_block_size; + if (pConfig->blocking) + { + ioctl(pConfig->input_device_handler, BLOCKING_ALL_IN_ONE_READ, &args_for_ioctl[0]); + } + else + { + ioctl(pConfig->input_device_handler, ALL_IN_ONE_READ, &args_for_ioctl[0]); + } + + if (size_to_write > 0) { + + if (pConfig->max_file_size_enable) { + if (size_writen + size_to_write > pConfig->max_file_size) { + size_writen = 0; + if (open_new_outputs (pConfig) < 0) { + exit (-4); + } + } + } + if (pConfig->output_file_1_enable) { + fwrite (pConfig->data, sizeof (uint32_t), (size_to_write >> 2), pConfig->output_file_1_handler); + } + if (pConfig->output_file_2_enable) { + fwrite (pConfig->data, sizeof (uint32_t), (size_to_write >> 2), pConfig->output_file_2_handler); + } + size_writen += size_to_write; + + if (pConfig->debug) { + idx = 0; + printf ("dma_to_raw_file: all_in_one_read : Data [%03u] = 0x%08x\n", idx, pConfig->data[idx]); + idx = (size_to_write >> 2) - 1; + printf ("dma_to_raw_file: all_in_one_read : Data [%03u] = 0x%08x\n", idx, pConfig->data[idx]); + } + } + + if (pConfig->verbose || size_to_write > 0) { + gettimeofday (&tv2, NULL); + + printf ("%s : all_in_one_read : reg = 0x%x %10d Total time = %f seconds\n", + pConfig->input_device_name, + size_to_write, + counter, + (double) (tv2.tv_usec - tv1.tv_usec) / 1000000 + + (double) (tv2.tv_sec - tv1.tv_sec)); + + } + counter++; + } while ((pConfig->max_count == 0)||(counter < pConfig->max_count)); + + return 0; +} + +int main(int argc, char *argv[]) +{ + ConfigDMAToRaw Config = + {.debug = false, + .toggle = false, + .blocking = false, + .verbose = false, + .redis = false, + .output_file_1_enable = false, + .output_file_2_enable = false, + //.max_block_size = 16384, + .max_block_size = 4095, + .max_count = 0, + .max_file_size_enable = false, + .max_file_size = 0, + .file_name_id = 0, + }; + pConfigDMAToRaw pConfig = &Config; + + manage_command_line (pConfig, argc, argv); + + if (pConfig->redis){ + if (manage_redis (pConfig) < 0) { + exit (-3); + } + } + + if (open_input_device (pConfig) < 0) { + exit(-1); + } + + if (open_new_outputs (pConfig) < 0) { + exit (-2); + } + + Init_DMA (pConfig); + + Read_DMA_And_Write_File (pConfig); + + close_all (pConfig); + + return 0; +} diff --git a/siphractrl/MK_WriteConfiguration_251210.py b/siphractrl/MK_WriteConfiguration_251210.py new file mode 100644 index 0000000..e973460 --- /dev/null +++ b/siphractrl/MK_WriteConfiguration_251210.py @@ -0,0 +1,30 @@ +import sys +import os +from d2a_lib import D2a +import time +import numpy as np + +if len(sys.argv) < 2: + + print() + print("ERROR! Please pass the binary configuration file as argument!") + print() + sys.exit(1) + +if not os.path.isfile(sys.argv[1]): + + print() + print(f"ERROR! Could not find file \"{sys.argv[1]}\". Please check path!") + print() + sys.exit(1) + +ucd_det = D2a() +ucd_det.sysclk(6) +ucd_det.reset('All') +ucd_det.writeSIPHRAfromFile(sys.argv[1], 'A') +time.sleep(1) +ucd_det.hold1Hz() +print() +print("Configuration",sys.argv[1],"written!") +print() + diff --git a/siphractrl/__pycache__/d2a_lib.cpython-314.pyc b/siphractrl/__pycache__/d2a_lib.cpython-314.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ffc7aa05c7a3c568f12b62394e2323848b2005c8 GIT binary patch literal 13671 zcmcgzYiu0Xb)MOsSf&Xi=mnOMXy}9g37h#wH~XY0`EiE{3~9a--$$ z`p&FmGIq?SK|(b_L?v;=CT+vkP9ileKIdNTDJu;Kc*?_nlX&|fLHIAKxDS7W%8KFdr zO}5I>UWWEkvXd{%Ri2Vtg6>BPf>yPzUcIiq zX>I+0l>3%yt;X7PO4%y#S!>t!(&|q6whAMm1ESM=&5HFO@~&a+Jnm}lJXXz!VAbYV zZTyg#Hb3~?0Z^Ep{H%4H0$V_VCik6HuZi1*=B?9524XCcu~aLq#?q;=#CVn&>hoRQ zYWIG1p!Y{pBNoY z8g>v(W^c+e#tnwblw}N@)iW6*?!3uWV$OCQn8T>BQjhwKa7wU5@TzAdXn8f!k~EJc zYhLVJvXy}oy+;^Jv*{W$lZYE<+FSRjskEi)XY@o;$NHCr{>wgFiAGcUq!EqUfoOCx z9nU7I9EwI?&g#kBn{YHbmSCopOr(rd8m~*D(RezBLUlB1TDp~pMRm(!iP5ZOn9(Tn zV|OdGWBc*8iS(q=mNsKLYdgWxpD|*V+2)`wI%t>{u+(NIG80UXS;@Beem$B@jJ9T` zn1bOP5Wku-z!!!8kT$b&xBX)wCcF_b&-Wg85L1y&q}Snu@4TOpI^acTwF;_Mxgd-} zem}KN4|-XAKM&)LUP!7+%ezTPL%9&SAlx9BdML%6lR3sNQzqdGpwdumVr{k{9^xQk1fR}5*gdejPYi#Ety7g zjMbq#Bc?KG=AAotGWuxZjzlV9fzoyBx7F&ts-{sPfEi)As`e}X<;qQ8RUEX`qP4$S zVMIbkU05lBG6IA~)<_@%V3$Xuv7~OA@kGpu8OdZcdReqf3mU;`^eLa@8)KVjAc0E! znY7nme+e7-xm5ds$0OI=6#$$)P^UHMB37c0T=_A;jBparc}^4}HPWw zpE+g5lBW|2y_LB%*s^6zW;&gp!?ri0V+1vIIX~So>^}eGXeq2*MbjG5JX|1}^(350 z5#rfEmK-IYCP7?FG+1y;(R_e@tx<#J#=C%~pdRGbI;3sZ0=%_U3!=453jv0;Qox8- z23W3z0V}i!V5L?LSfy0}R%?}jo3tvx8m$_zR@($vr_}(~YqeGb6h)&}2kLLJz1{ou z=@Wsjbjs2bDVU&SQXR|2QdT038Z%zbCYTW?(*>3=*q9zO)Uyd|LY>fA{46M}!YU+U zhN)T;ES(*nP>&C^2G)p52`~hqP*#2o)}9d>*65a#d2tyMPUNw>TF~GYxz5iaF)C&~ zS#MN0km~>ek=(u~@+G%LfCDw;JwfZQd))DwsCitm91>Fb88k2bbJuMWu(tw-$DPcb zr3v43lJpOMPLLj1_iWAYcu+{?g?&&sK`MXUQ%r%3z=3q#3UU;Sk4)wFNakA=_6WOj zbB}{v8-;|YN!Tnj@qf>L=8$)@kPubjB&k&X_=Gq#Yom~nPLguv&!9Bn(YzVyX(=Hl zgby7CjQQqS1Fu>{_xxgoIw zaRd6?U{=?F?LYEL%*YUDaytqk=01t-8^|QOjWf0vbv@8=jt%zM*{J6)GOS9g3Vke(v~UX!Bh~_V2h= zv-#p{UwiHAug`XWrDqxMdd~Hn?>pBwzjeNIF|=j5ar109wSBekgG!9X62HCg6C!o@ z1rdWQ>KDA%L)(9Q*NfV3@0S&S@f-6Pw*C2sx)kC2Er$=v?^f2L_-;#QN!K>vrw4;w zvj3W(py`?t>}v8}t1RiN_g<@)sk|-N^@R7@-jc2Z-fIVBl-G!H&fN#J5j;!N3H(*tV!<9^d|zEp~uxvgD+31+#;&F zrG8qE$Jr^B%p|m*p-vi;X*Q*Thv9y!U~82)xi!!%IlK%f$vADx894QpEiug4?By(K zOEeSPjZazUIZ>y+J_rS8Pq#Ki9iIgNdn$sz^t^ITIbV9NbS}QAG~7~3XXEE5&P|-3 zJU2Ptd82I0qSCaigw}Lg4p*Lk<=iXhUpx2O{Bt+LP1hC2p01%dsb>6zRv2IE0ocnc zaCFK&L!OVMRGtsqF@jLYCp>cUOurQ19;`Z%K5LvY7*LyT@$qG1!lZMo)g8e>vd_8 zk%lNp@Jzqva5Z5LEX&ehOdYX7Dmys}T&Kr!m@ee+0tWw3{PIv14xnj}WRQ3`&cUZm z9=)DwRRhf;XL#WJd3+$0d1Bf~k3l?Wl&iam2zcd$ynf<;if~Ok#Cye^Sa_X2w*%BK z7bcexp;qhTn9pnl;FPMayV!T3Z(+yZPhOtIAtuZ{T-S^Qo=u!K;Ckt1 z?ju@Mgv4mDvJ5^Mkqi8pHPo>*Q+3q@V+8g~ zQhiuIOiMDD7mI3yLex8!{tLCAWuo}HMpZ%2{w`Bm4AFYw{=L%8WV7Jy%_7m(hx(&Vj!j*n&iF`kmx`&)R&~Izc&Av>&>1z+0)q@fz*|h-4d0CqhMmS7Sjt^u zl9av7x!zXXPqagw{e!*vnmn3KB;)KQPyv|+?lu}IuZn~%^ZnWW{0_K~*dgu>45qoA zGRf^k6kHGp2e^@N#D?pl)%p}Vn_~cwn=)aI+}sXEzR|N7Z2V=ob~)1ceq{SwuU*OwxXqUEkn*uHzd`n^!o?VR>{_C`%Rv=_`i*_~H!Di1rN zmuF(EOJe9H`~DQ)L~$LaiL2sPAVQ*>R|R`|z@FaeqX7_!^Br)nX263JNL#iBo;QuL zY?A18+AuPl?VziW?aL;0=H@@+36rvk@zwx4h|Tn0_Odj7?O@lS_H4AXyIYIyvwgg> z@3O}s5M`T8`W`pW1#d4B2hr$Y? 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Automatically stores a json file with the relevant metadata. +# Written by: Oscar Rosero (KTH) +# .... +# Date: 02/2026 + +import argparse +import subprocess +import json +from pathlib import Path +import time +from datetime import datetime, timezone + +def parse_args(): + parser = argparse.ArgumentParser( + description="Acquire data from SIPHRA ASIC using dma_to_raw_file and store metadata." + ) + + parser.add_argument("-o", "--output", + required=True, + help="Base name for output files (without extension).") + parser.add_argument("-c", "--counts", type=int, default=100_000, + help="Total number of events to acquire.") + parser.add_argument("--active-chs", nargs="*", type=int, default=[], + help="SIPHRA active channels. Receives all active channel numbers (1 -16) separated by a space.") + parser.add_argument("--sipm-chs", nargs="*", default='', + help="Configuration of SIPM channels. One or more strings explaining this configuration.") + parser.add_argument("--source", type=str, default='[NOT SPECIFIED]', + help="A single string containing \'Background\' or any radioactive source present") + parser.add_argument("--source-description", type=str, default='[NOT SPECIFIED]', + help="A single string.") + parser.add_argument("--notes", type=str, default='[NONE]', + help="A single string.") + parser.add_argument("--siphra-config-file", type=str, default='D2a/Ongoing.txt', + help="By default, it takes the \'Ongoing.txt\' file in the \'D2a\' directory and writes its contents to the metadata file. If any other configuration is used, the path to the text file containing it must be specified here.") + parser.add_argument("-s", "--size", type=int, default=4095, + help="[DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS]. Block size. Default is 4095.") + parser.add_argument("--device", default="/dev/D2A_DMA", + help = "[DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS].") + + + return parser.parse_args() + +def run_acquisition(args): + output_base = Path(args.output).resolve() + dat_file = output_base.with_suffix(".dat") + + cmd = [ + "./dma_to_raw_file", + "-i", args.device, + "-o", str(dat_file), + "-s", str(args.size), + "-v", + "-c", str(args.counts), + "-b" + ] + + start = time.time() + subprocess.run(cmd, check=True) + end = time.time() + + exposure = end - start + return exposure, output_base + +def write_metadata(output_base, exposure, args): + with open(args.siphra_config_file) as f: + siphra_config = f.readline().strip() + metadata = { + "schema-version": "1.0", + + "acquisition": { + "timestamp_utc": datetime.now(timezone.utc).isoformat(), + "exposure_sec": exposure, + "counts": args.counts, + "active_chs": args.active_chs, + "sipm_chs": args.sipm_chs, + }, + + "source": { + "type": args.source, + "description": args.source_description, + }, + + "SIPHRA_config":{ + "cmd_string": siphra_config, + }, + + "additional_info": { + "data_file": str(output_base.with_suffix(".dat")), + "notes": args.notes, + } + } + + json_file = output_base.with_suffix(".json") + with open(json_file, "w") as f: + json.dump(metadata, f, indent=4) + +def main(): + args = parse_args() + exposure, output_base = run_acquisition(args) + write_metadata(output_base, exposure, args) + print(f"\n\nAcquisition complete.\n\tWritten to: {output_base}\n\tTotal exposure: {exposure:,.2f} seconds.") + +if __name__ == "__main__": + main() + diff --git a/siphractrl/d2a_lib.py b/siphractrl/d2a_lib.py new file mode 100644 index 0000000..1dee38e --- /dev/null +++ b/siphractrl/d2a_lib.py @@ -0,0 +1,320 @@ +'''Script to configure UCD D2 SIPHRA ASICs and reconfigure during flight''' +import mmap +import logging +from construct import BitStruct, Nibble, BitsInteger, ByteSwapped, BitsSwapped +import time +import spidev + +# define Python user-defined exceptions + + +class SPIError(Exception): + 'spidev1.0 not available' + + +class UIOError(Exception): + 'uio0 not available' + + +class SIPHRAWriteError(Exception): + 'SIPHRA write error' + def __init__(self, chip, reg): + self.chip = chip + self.reg = reg + + + + + + +SIPHRA_REG_LENS = [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 14, 24, 23, 6, 8, 18, 6, 19, 15, 6, 2, 2] + +def compareUpTo(a, b, len): + a_zeroed = int.from_bytes(a, byteorder='big') & (2**len - 1) + b_zeroed = int.from_bytes(b, byteorder='big') & (2**len - 1) + return a_zeroed == b_zeroed + + +CHIP2BIN = {'A':1, 'B':2, 'C':4, 'D':8, 'All':15} +ANTICHIP2BIN = {'A':0b1110, 'B':0b1101, 'C':0b1011, 'D':0b0111, 'All':0b0000} +HOLDRATES = {'Off':0, '1Hz':2, '1kHz':1} + +CTRL_ADDR_0 = 4096 +CTRL_0 = BitStruct( + 'hold' / Nibble, + 'reset' / Nibble, + 'cs' / Nibble, + 'sysclk' / Nibble, + ) + +DATA_ADDR_0 = 4104 +DATA_0 = BitsSwapped( BitStruct( + 'error' / Nibble, + 'tempA' / BitsInteger(12), + 'tempB' / BitsInteger(12), + 'pad' / Nibble, + )) + +DATA_ADDR_1 = 0 +DATA_1 = BitsSwapped(BitStruct( + 'tempC' / BitsInteger(12), + 'tempD' / BitsInteger(12) + + )) + + +class D2a: + ''' + Contains all fucntions required to interface with hardware devices through OS. + ''' + def __init__(self, uio='/dev/uio0', spi='/dev/spi'): + try: + self.uio = uio + with open(self.uio, "r+b", 0) as f: + self.reg = mmap.mmap(f.fileno(), length = 4108) + self.deassertCS() + except Exception as exc: + raise UIOError from exc + + try: + self.spi = spidev.SpiDev() + self.spi.open(1,0) + self.spi.mode = 0 + except Exception as exc: + raise SPIError from exc + + def readMMap(self, addr, len=2): + ''' + Read [len] bytes at [addr] from the memory mapped uio file. + ''' + self.reg.seek(addr) + print(self.reg.read(4)) + self.reg.seek(addr) + return self.reg.read(4) + + def writeMMap(self, val, addr): + ''' + Write however many bytes are supplied to the memory mapped + uio file beginning at [addr]. + ''' + self.reg.seek(addr) + self.reg.write(val) + + def readStruct(self, addr, struct): + ''' + Read the appropriate number of bytes from the memmory mapped + uio file beginning at [addr] and parse them through [struct]. + ''' + readBytes = self.readMMap(addr, len = struct.sizeof()) + parsedStruct = struct.parse(readBytes) + return parsedStruct + + def readParam(self, param, addr, struct): + ''' + Like reasdStruct, but returns the value for a single parameter + contained within the struct. + ''' + parsedStruct = self.readStruct(addr, struct) + print(parsedStruct) + return parsedStruct[param] + + def writeParam(self, param, addr, struct, value, clearbits=False): + ''' + Writes a value to a parameter stored in a register. + If the value given is one of the keys of CHIP2BIN (i.e. 'A', 'B', 'C', 'D', 'All'), + then only the bit corresponding to that chip will be set, respecting the existing values + of other chips' bits. Setting clearbits to True clears bits instead of setting them. + ''' + parsedStruct = self.readStruct(addr, struct) + + if value in CHIP2BIN: + if clearbits: + parsedStruct[param] &= ANTICHIP2BIN[value] # clear the bit corresponding to chip. + else: + parsedStruct[param] |= CHIP2BIN[value] # set the bit corresponding to chip. + + else: + parsedStruct[param] = value + + bytesToWrite = struct.build(parsedStruct) + self.writeMMap(bytesToWrite, addr) + + + def keepReset(self, chip): + ''' + Set a reset pin high. + Useful to keep one particular SIPHRA disabled. + ''' + self.writeParam('reset', CTRL_ADDR_0, CTRL_0, chip) + + def releaseReset(self, chip): + ''' + Set a reset pin low. + Used to activate a SIPHRA which had previously been disabled. + ''' + self.writeParam('reset', CTRL_ADDR_0, CTRL_0, chip, clearbits = True) + + def reset(self, chip, toggle_time=0.01): + ''' + Set a reset pin high and then low again after toggle_time. + I.e. actually reset a SIPHRA. + ''' + self.keepReset(chip) + time.sleep(toggle_time) + self.releaseReset(chip) + + + def holdOff(self): + ''' + Disable external triggering. + ''' + self.writeParam('hold', CTRL_ADDR_0, CTRL_0, HOLDRATES['Off']) + + def hold1Hz(self): + ''' + Enable external triggering at a rate of 1Hz. + ''' + self.writeParam('hold', CTRL_ADDR_0, CTRL_0, HOLDRATES['1Hz']) + + def hold1kHz(self): + ''' + Enable external triggering at a rate of 1kHz. + ''' + self.writeParam('hold', CTRL_ADDR_0, CTRL_0, HOLDRATES['1kHz']) + + + def sysclk(self, freq): + ''' + Write a value to the sysclk register to control the sysclk frequency. + + TODO: Make a list of the values and corresponding frequencies. + ''' + self.writeParam('sysclk', CTRL_ADDR_0, CTRL_0, freq) + + + def assertCS(self, chip): + ''' + CS is active low, so we want to set the bit corresponding to chip low + and make sure all the others are high. Probably shouldn't call this with 'All', + but I won't stop you! + ''' + self.writeParam('cs', CTRL_ADDR_0, CTRL_0, ANTICHIP2BIN[chip]) + + def deassertCS(self): + ''' + CS is active low. There isn't really a use case to deassert one at a time. + So we'll just deassert everything by setting them high. + ''' + self.writeParam('cs', CTRL_ADDR_0, CTRL_0, 15) + + + def error(self): + ''' + Returns a single value error 0-15 corresponding to + bitmask of error pins on SIPHRAs A, B, C, D. + ''' + return self.readParam('error', DATA_ADDR_0, DATA_0) + + + def temp(self): + ''' + Returns a dictionary of the four SIPHRA temperatures. + Temperature values are in raw ADC counts. + ''' + tempAB = self.readStruct(DATA_ADDR_0, DATA_0) + tempCD = self.readStruct(DATA_ADDR_1, DATA_1) + temps = { + 'A': tempAB['tempA'], + 'B': tempAB['tempB'], + 'C': tempCD['tempC'], + 'D': tempCD['tempD'], + } + return temps + + + def spiXfer(self, bytelist, chip): + ''' + Version of spi.xfer2 that asserts CS using the D2a GPIOs. + ''' + self.assertCS(chip) + readback = self.spi.xfer2(bytelist, 100_000) + self.deassertCS() + return readback + + def writeSIPHRA(self, val, addr, chip): + ''' + Write a SIPHRA register. + + val: 4-byte bytearray with register contents. + addr: register address. + chip: 'A', 'B', 'C', 'D' or 'All'. + 'All' definitely not recommended. + ''' + write_addr = addr << 1 | 1 + bytes_to_send = [write_addr] + list(val) + self.spiXfer(bytes_to_send, chip) + + def readSIPHRA(self, addr, chip): + ''' + Read a register from SIPHRA. + + addr: register address. + chip: 'A', 'B', 'C', 'D' or 'All'. + 'All' definitely not recommended. + + Returns a four-byte bytearray with register contents. + ''' + read_addr = addr << 1 + bytes_to_send = [read_addr] + [0] * 4 + return bytes(self.spiXfer(bytes_to_send, chip)[1:]) + + def writeSIPHRAwithCheck(self, val, addr, chip): + ''' + Keep writing a register to SIPHRA until + the value you get when reading it back out + matches the expected input. + + TODO: Make the function give up eventually + or try something else like resetting SIPHRA. + ''' + self.writeSIPHRA(val, addr, chip) + remaining_tries = 3 + match = False + while remaining_tries > 0 and match is False: + readback = self.readSIPHRA(addr, chip) + match = compareUpTo(val, readback, SIPHRA_REG_LENS[addr]) + if match is True: + break + else: + remaining_tries -= 1 + if remaining_tries == 0 and match is False: + raise SIPHRAWriteError(chip = chip,reg = addr) + + def writeSIPHRAfromFile(self, filename, chip): + ''' + Write registers to a given SIPHRA chip. + + Assumes every four bytes in a binary file + is a new register value. Will start at + register 0 and keep going while there are + bytes in the file. + + If 'All' is selected for chip, every SIPHRA + gets the same configuration. + ''' + chips = ['A', 'B', 'C', 'D'] if chip == 'All' else [chip] + + vals = [] + with open(filename, 'r+b', 0) as f: + while (val := f.read(4)): + vals.append(val) + + for chip in chips: + for addr, val in enumerate(vals): + print("value:",val) + print("address:",addr) + print("chp:",chip) + self.writeSIPHRAwithCheck(val, addr, chip) + + + diff --git a/siphractrl/regs_bit_structure.py b/siphractrl/regs_bit_structure.py new file mode 100644 index 0000000..83777ea --- /dev/null +++ b/siphractrl/regs_bit_structure.py @@ -0,0 +1,104 @@ +from construct import BitStruct, BitsInteger, Padding, Flag + +# ---------- Bit Structures of SIPHRA registers ---------- + +# Registers 0x00 - 0x0F: Channel configuration registers +CHANNEL = BitStruct( + Padding(6), + 'cmis_detector_voffset' / BitsInteger(8), + 'cmis_detector_ioffset' / BitsInteger(3), + 'cmis_impedance_reduction' / BitsInteger(1), + 'cal_select_channel' / Flag, + 'qc_threshold' / BitsInteger(8), + 'qc_hysteresis' / BitsInteger(3), + 'pu_channel' / Flag, + 'enable_triggering' / Flag, +) + +# Register 0x10: Summing channel configuration registers +SUMM_CHANNEL = BitStruct( + Padding(18), + 'cal_select_channel' / Flag, + 'qc_threshold' / BitsInteger(8), + 'qc_hysteresis' / BitsInteger(3), + 'pu_channel' / Flag, + 'enable_triggering' / Flag, +) + +# Register 0x11: Configuration register - channel_config +CHANNEL_CONFIG = BitStruct( + Padding(8), + 'cmis_gain' / BitsInteger(3), + 'ci_gain' / BitsInteger(2), + 'ci_compmode' / BitsInteger(1), + 'shaper_bias' / BitsInteger(4), + 'shaper_feedback_cap' / BitsInteger(4), + 'shaper_feedback_res' / BitsInteger(3), + 'shaper_hold_cap' / BitsInteger(3), + 'shaper_input_cap' / BitsInteger(4), +) + +# Register 0x12: Configuration register - channel_control +CHANNEL_CONTROL = BitStruct( + Padding(9), + 'ci_feedback_dac' / BitsInteger(8), + 'ci_use_reset' / BitsInteger(1), + 'th_select_input' / BitsInteger(1), + 'cb_select_input' / BitsInteger(2), + 'ms_select_input' / BitsInteger(1), + 'cc_enable_dcc' / Flag, + 'cc_threshold' / BitsInteger(8), + 'pt_100_enable_excitation' / Flag, +) + +# Register 0x13: Configuration register - ADC configuration +ADC_CONFIG = BitStruct( + Padding(26), + 'analog_readout_mode' / BitsInteger(1), + 'adc_sample_duration' / BitsInteger(4), + 'adc_mode' / BitsInteger(1), +) + +# Register 0x14: Gain Calibration Unit, Voltage DAC setting +CAL_DAC = BitStruct( + Padding(24), + 'cal_dac' / BitsInteger(8), +) + +# Register 0x15: Power modules +PD_MODULES = BitStruct( + Padding(14), + 'pu_sum_cc' / Flag, + 'pu_sum_ci' / Flag, + 'pu_sum_sha' / Flag, + 'pu_sum_th' / Flag, + 'pu_sum_qc' / Flag, + 'pu_sum_cb' / Flag, + 'pu_cmis' / Flag, + 'pu_cc' / Flag, + 'pu_ci_fb_dac' / Flag, + 'pu_ci' / Flag, + 'pu_sha' / Flag, + 'pu_th' / Flag, + 'pu_qc' / Flag, + 'pu_cb' / Flag, + 'pu_trigger_output_enable' / Flag, + 'pu_adc_ref' / Flag, + 'pu_pt100' / Flag, + 'pu_mb' / Flag, +) + +# Register 0x16 +CARL_CTRL = BitStruct( + Padding(26), + 'cal_cv_range_low' / Flag, + 'cal_cv_range_mid' / Flag, + 'cal_cv_range_high' / Flag, + 'cal_trigger_select' / Flag, + 'cal_mode' / BitsInteger(2) +) + +# Register 0x17 +READOUT_FIXED_LIST = BitStruct( + Padding(8), +) \ No newline at end of file diff --git a/siphractrl/siphra_controller.py b/siphractrl/siphra_controller.py new file mode 100644 index 0000000..bcd19cb --- /dev/null +++ b/siphractrl/siphra_controller.py @@ -0,0 +1,112 @@ +from .d2a_lib import * +from .regs_bit_structure import * + +CH_ADDRS = [] +SIPHRA_RETURN_SIZE = 15 # bytes. Returned by SIPHRA when D2a.readSIPHRA is called +# TODO: Check the size of the packet returned by D2a.readSIPHRA +REG_SIZE = 4 # bytes. Size of the bytearray passed to D2a.writeSIPHRA + +# Register map classes + +class SIPHRARegister: + def __init__(self, addr, structure): + self.addr = addr + self.structure = structure + + def __getitem__(self, param_name): + pass + + def __contains__(self, param_name): + '''True if the register has a parameter named parameter.''' + params = self.structure.subcon.subcons + param_names = [param.name for param in params if param.name] + return param_name in param_names + + def parse(self, value): + return self.structure.parse(value) + + def set_param(self, param_name, value, reg_current_value): + old_register = parse(reg_current_value) + + +class SIPHRARegMap: + def __init__(self): + self._registers = {} + + for i in range(1,17): + self._registers[f"ch{i}"] = SIPHRARegister(i, CHANNEL) + + self._registers["summ"] = SIPHRARegister(0x10, SUMM_CHANNEL) + self._registers["ch_config"] = SIPHRARegister(0x11, CHANNEL_CONFIG) + + def __getitem__(self, name): + return self._registers[name] + + def __getattr__(self, name): + return self._registers[name] + + def __iter__(self): + return iter(self._registers.values()) + + def __len__(self): + return len(self._registers) + + def __contains__(self, name): + return name in self._registers + + def items(self): + return self._registers.items() + + def keys(self): + return self._registers.keys() + + def values(self): + return self._registers.values() + + +class SIPHRA: + def __init__(self, d2a: D2a): + self._d2a = d2a + self._regs = SIPHRARegMap() + + def get_reg_value(self, reg_name, chip='A'): + addr = self._regs[reg_name].addr + reg_value = self._d2a.readSIPHRA(addr, chip)[SIPHRA_RETURN_SIZE - REG_SIZE:] + return reg_value + + def read_register(self, reg_name, chip='A'): + reg_value = self.get_reg_value(reg_name, chip) + strct = self._regs[reg_name].structure + parsed_struct = strct.parse(reg_value) + return parsed_struct + + def _find_reg_containing_param(self, parameter, ch: int=0): + ''' + Returns the address of the register containing the given parameter. + :param parameter: Name of the desired parameter. + :param ch: If the parameter is in one of the 16+1 channel addresses, this argument is used for disambiguation. ``ch`` is a number between 1 and 16 if the parameter belongs to one of the ``ctrl_ch`` registers; it is 17 if the parameter belongs to the summing channel control register, and is defaulted to 0 if the parameter belongs to any other register. + :return: The address of the register containing the given parameter. + ''' + reg_addr = None + if not 0 < ch < 17: + raise ValueError(f"Channel {ch} is out of range. Use channels 1-16 and 17 for summing channel") + if ch == 0: # Not a channel register + for reg in self._regs: + if parameter in reg: + reg_addr = reg.addr + else: # Channel register + # Verify that the given register exists + register = self._regs[f"ch{ch}"] + if parameter in register: reg_addr = register.addr + if not reg_addr: + raise NameError(f"Parameter {parameter} does not exist or the register containing it is not implemented.") + return reg_addr + + + + def read_param(self, parameter, ch=0, reg_name=None, chip='A'): + if reg_name: + reg_addr = self._regs[reg_name].addr + else: + reg_addr = self._find_reg_containing_param(parameter, ch) + From a408cc0cfbfdb605bfbed536be8ca5805dfa4753 Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Fri, 27 Mar 2026 10:34:48 +0100 Subject: [PATCH 07/18] update --- noteboks/22Na_60Co_bg-subtraction_calib.ipynb | 261 ---- noteboks/260204_CC_BG_comparison.ipynb | 296 ----- noteboks/260205.ipynb | 288 ----- noteboks/260206_CMIS_voffset.ipynb | 154 --- noteboks/260209_Cs137_BGsubtraction.ipynb | 253 ---- noteboks/260212.ipynb | 214 --- noteboks/CT_Cs137_calib.ipynb | 731 ----------- noteboks/QT_compare_channels.ipynb | 532 -------- noteboks/Untitled.ipynb | 229 ---- noteboks/Voltage_dependency.ipynb | 695 ---------- noteboks/banana.ipynb | 464 ------- noteboks/baseline_subtraction.ipynb | 1143 ----------------- noteboks/energyResolution.ipynb | 308 ----- noteboks/etcTests/converters_explore.ipynb | 313 ----- noteboks/etcTests/siphraAcq_test_json.ipynb | 125 -- noteboks/gainTesting.ipynb | 687 ---------- noteboks/histogram_summed.ipynb | 294 ----- noteboks/img/etc/CMIS_internal.png | Bin 54280 -> 0 bytes noteboks/img/etc/SIPHRA_arch.png | Bin 905852 -> 0 bytes noteboks/include_project_root.py | 1 - noteboks/root_tests/hist010.ipynb | 142 -- noteboks/root_tests/hist010_TH1_two_scales.py | 44 - noteboks/spectra_current_thrsh.ipynb | 529 -------- noteboks/spectrum_plotter.ipynb | 1022 --------------- .../__pycache__/metadata.cpython-314.pyc | Bin 2877 -> 2877 bytes siphractl/ComptonCam_DMA_To_RAW_File/Makefile | 17 - .../src/dma_to_raw_file.c | 437 ------- siphractl/MK_WriteConfiguration_251210.py | 30 - siphractl/__pycache__/d2a_lib.cpython-314.pyc | Bin 13671 -> 0 bytes siphractl/acquire.py | 107 -- siphractl/d2a_lib.py | 320 ----- siphractl/regs_bit_structure.py | 91 -- siphractl/siphra_controller.py | 112 -- 33 files changed, 9839 deletions(-) delete mode 100644 noteboks/22Na_60Co_bg-subtraction_calib.ipynb delete mode 100644 noteboks/260204_CC_BG_comparison.ipynb delete mode 100644 noteboks/260205.ipynb delete mode 100644 noteboks/260206_CMIS_voffset.ipynb delete mode 100644 noteboks/260209_Cs137_BGsubtraction.ipynb delete mode 100644 noteboks/260212.ipynb delete mode 100644 noteboks/CT_Cs137_calib.ipynb delete mode 100644 noteboks/QT_compare_channels.ipynb delete mode 100644 noteboks/Untitled.ipynb delete mode 100644 noteboks/Voltage_dependency.ipynb delete mode 100644 noteboks/banana.ipynb delete mode 100644 noteboks/baseline_subtraction.ipynb delete mode 100644 noteboks/energyResolution.ipynb delete mode 100644 noteboks/etcTests/converters_explore.ipynb delete mode 100644 noteboks/etcTests/siphraAcq_test_json.ipynb delete mode 100644 noteboks/gainTesting.ipynb delete mode 100644 noteboks/histogram_summed.ipynb delete mode 100644 noteboks/img/etc/CMIS_internal.png delete mode 100644 noteboks/img/etc/SIPHRA_arch.png delete mode 100644 noteboks/include_project_root.py delete mode 100644 noteboks/root_tests/hist010.ipynb delete mode 100644 noteboks/root_tests/hist010_TH1_two_scales.py delete mode 100644 noteboks/spectra_current_thrsh.ipynb delete mode 100644 noteboks/spectrum_plotter.ipynb delete mode 100644 siphractl/ComptonCam_DMA_To_RAW_File/Makefile delete mode 100644 siphractl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c delete mode 100644 siphractl/MK_WriteConfiguration_251210.py delete mode 100644 siphractl/__pycache__/d2a_lib.cpython-314.pyc delete mode 100644 siphractl/acquire.py delete mode 100644 siphractl/d2a_lib.py delete mode 100644 siphractl/regs_bit_structure.py delete mode 100644 siphractl/siphra_controller.py diff --git a/noteboks/22Na_60Co_bg-subtraction_calib.ipynb b/noteboks/22Na_60Co_bg-subtraction_calib.ipynb deleted file mode 100644 index a84fdbc..0000000 --- a/noteboks/22Na_60Co_bg-subtraction_calib.ipynb +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cda6a9c1-c4c0-4e69-b50d-7dcb1b8434f2", - "metadata": {}, - "source": [ - "# Calibrating using 22-Na and 60-Co\n", - "\n", - "Baseline-subtracted acquisitions were used to determine if 22-Na and 60-Co were siutable peaks for energy calibration. We also included a 137-Cs source in the acquisition, with the intention of using the peak to check the accuracy of the calibration. \n", - "\n", - "The baseline-subtracted histograms of an acquisition with 2 active channels were obtained. The baseline value for each channel is the mean of the readings from all the events triggered ONLY by external HOLD assertion. This value is subtracted from the values of each \"real\" event (triggered from internal HOLD).\n", - "\n", - "The dataset for this analysis is a background acquisition of 500 000 events. SIPHRA channels 2 and 14 were connected to SiPM channels 1-4 and 5-8, respectively, using charge comparator, with a threshold value of 20. The source acquisition registered 1 000 000 events. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "initial_id", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:37:32.276396Z", - "start_time": "2026-02-10T09:37:32.169808Z" - } - }, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "\n", - "from processing import SiphraAcquisition\n", - "from analysis import fit_peak_expbg" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a42cc03d-bbcb-43b2-80ef-d06eaf81ab7a", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "CS137_MEV = 0.662\n", - "NA22_MEV = 0.511\n", - "CO60_MEV_1 = 1.173\n", - "CO60_MEV_2 = 1.332\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "68a9cd08dc972f65", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:37:35.457562Z", - "start_time": "2026-02-10T09:37:33.669215Z" - } - }, - "outputs": [], - "source": [ - "# Datasets\n", - "acq_background = SiphraAcquisition(project_root+'/data/260223/5_SiPM_Ch1-4_Ch2_Ch5-8_Ch14_Active_1-16_PT100_background.csv',\n", - " active_chs=[2,14],\n", - " exposure_sec=277.478)\n", - "acq_sources = SiphraAcquisition(project_root+'/data/260223/3_SiPM_Ch1-4_Ch2_Ch5-8_Ch14_Active_1-16_PT100_Sources_Cs137_Co60_Na22.csv',\n", - " active_chs=[2,14],\n", - " exposure_sec=448.742)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cae21a3ee684d69f", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:43:25.401070Z", - "start_time": "2026-02-10T09:43:24.995723Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "SUMMED_XR=5000 # Right limit of the summed channel's x-axis\n", - "\n", - "c = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "names=['Background radiation', '22Na and 60Co', 'Background subtracted']\n", - "hists = []\n", - "\n", - "lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", - "\n", - "Yinit = 0.82 # For stat boxes\n", - "\n", - "for idx, (acq, name) in enumerate(zip([acq_background, acq_sources], names)):\n", - " # print(f\"Current file: {acq.filepath.name}\")\n", - " ch = 'Summed' #acq.active_chs[0]\n", - " hist = ROOT.TH1F(f\"{ch} {name}\", \"\", int(BITS9*SUMMED_XR/BITS12), 0, SUMMED_XR)\n", - " hist.Fill(acq[ch]/len(acq.active_chs))\n", - " hist.Scale(1/acq.exposure)\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Counts/s\")\n", - " hist.SetLineColor(colors[idx])\n", - " hist.SetTitle(\"Background source subtraction\")\n", - " # hist.GetYaxis().SetRangeUser(0, 1e4)\n", - " lg.SetHeader(\"SIPHRA Channel\")\n", - " lg.AddEntry(hist, f\"{ch} {name}\", 'l')\n", - " hists.append(hist)\n", - " hist.Draw('sames hist')\n", - " \n", - "idx+=1\n", - "h_diff = hists[1].Clone(\"Background subtracted\")\n", - "h_diff.Add(hists[0], -1)\n", - "h_diff.SetLineColor(colors[idx])\n", - "lg.AddEntry(h_diff, f\"{ch} {name}\", 'l')\n", - "hists.append(h_diff)\n", - "h_diff.Draw('sames hist')\n", - "c.SetLogy()\n", - "ROOT.gPad.Update()\n", - "for i, sp in enumerate(hists):\n", - " st = sp.FindObject(\"stats\")\n", - " # print(type(st))\n", - " st.SetY1NDC(Yinit - i*0.08)\n", - " st.SetY2NDC(0.1)\n", - "lg.Draw()\n", - "c.Draw()\n", - "ROOT.gPad.Modified()\n", - "ROOT.gPad.Update()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "4fe40546-908d-46cd-868c-f4c3e9bd0902", - "metadata": {}, - "source": [ - "# Bad resolution\n", - "\n", - "It seems like the energy peaks from $^{60}$Co, at energies 1.173 MeV and 1.332 MeV are too close to be distinguishable, making them bad candidates for calibration. The resolution is pretty bad, which is probably not good. \n", - "\n", - "$^{22}$Na has a second peak in between these peaks, at 1.275 MeV. Is this peak showing up stronger than expected, causing issues?\n", - "How can we get better resolution?\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40216972-edac-4bb7-b6d1-90992749a62b", - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/260204_CC_BG_comparison.ipynb b/noteboks/260204_CC_BG_comparison.ipynb deleted file mode 100644 index 4e04e54..0000000 --- a/noteboks/260204_CC_BG_comparison.ipynb +++ /dev/null @@ -1,296 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T10:44:04.423897Z", - "start_time": "2026-02-06T10:44:04.420846Z" - } - }, - "source": [ - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ], - "outputs": [], - "execution_count": 2 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "# Current Comparator background spectra\n", - "\n", - "We acquired spectra from channels 2, 6, 10 and 14 on the SIPHRA by connecting the two available probes, which output the sum of the signals at the SiPM channels 1 - 4 and 5 - 8, respectively. We present a comparison of the data thus acquired. In this part we used the charge comparator on the SIPHRA to trigger readout. Since the charge comparator threshold is global, the only parameter in which the configuration of the active channel and the inactive ones differed was the value of the parameter `enable_triggering`. All other parameters are reported in the following table:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Parameter
Value
`cmis_detector_voffset`
127
`cmis_detector_ioffset`
7
`cc_threshold`
1
" - ], - "id": "f22322a36b757cda" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T10:44:08.728130Z", - "start_time": "2026-02-06T10:44:08.023861Z" - } - }, - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "\n", - "dfs = []\n", - "dfs.append(pd.read_csv('../data/260204/1_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260204/2_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260204/3_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260204/4_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_Background.csv'))\n", - "\n", - "# dfs[0]" - ], - "id": "75bebada911337a", - "outputs": [], - "execution_count": 3 - }, - { - "cell_type": "code", - "id": "cb4c1aad-40db-456b-a316-8ce713563841", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T10:50:04.960837Z", - "start_time": "2026-02-06T10:50:04.332499Z" - } - }, - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "N_BINS = 512\n", - "BITS_12 = 2**12\n", - "\n", - "summed_spectra = [df[ch].tolist() for df, ch in zip(dfs, ['Ch14', 'Ch10', 'Ch6', 'Ch2'])]" - ], - "outputs": [], - "execution_count": 4 - }, - { - "cell_type": "code", - "id": "436876e4-6c49-4cc6-9552-6551a7059e27", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T10:50:07.969066Z", - "start_time": "2026-02-06T10:50:06.517005Z" - } - }, - "source": [ - "import numpy as np\n", - "times = [164.365, 148.329, 161.756, 119.645]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.14', 'Ch.10', 'Ch.6', 'Ch.2']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Current comparator background spectra, SiPM-Channels 5-8')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 5 - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "e68ab9ee-bdeb-4c34-b3ef-e032a4b3a6ec", - "metadata": {}, - "outputs": [], - "source": [ - "dfs_ch1_4 = []\n", - "dfs_ch1_4.append(pd.read_csv('../data/260204/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_Background.csv'))\n", - "dfs_ch1_4.append(pd.read_csv('../data/260204/6_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_Background.csv'))\n", - "dfs_ch1_4.append(pd.read_csv('../data/260204/7_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_Background.csv'))\n", - "dfs_ch1_4.append(pd.read_csv('../data/260204/8_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_Background.csv'))\n", - "\n", - "dfs_ch1_4 = dfs_ch1_4[::-1]\n", - "single_ch_spectra = [df[ch].tolist() for df, ch in zip(dfs_ch1_4, ['Ch14', 'Ch10', 'Ch6', 'Ch2'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "97eb2049-7aab-4804-a96b-555d5d4c84f9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Jc7ks6Lv4vBZZsPoQr7+q410bsX8RiInMlzgXRFa+uroW98VxFMdTZEdFICHat1783il7TaL+q+uGrSxwqu29U5dj2hBi/yKLWb7HgYuJwEi8HtG7QU3tMK+++mr5S4gIsqr6Eh4aGmqrG9FVlDB58mT5HhDtWi8mylTXtryibkSQKbr8EueZyFQ2NvE+F+fVxce5/K8OFxN1K9qwimBddCkm2lE3hMgyl9+nOP9Ee36RSRftecVU1s2ZeC+K84RcDwNZchri5yvxgSk+lMWFIuInfvHNW1zoIP7ICuKiD5Et+/XXX+UfAvGzXUO76BE/E4qg5L777pMfjGLf4iIMEcDUlfjDJJ4nyiL+iFSVqRUBusjyiT/E4kImsU8ROJQFHeIDv+zCGXExhciyiO2KrMalEH+IRXZEvLZ//vlHZkbKfhoVwYgIfMRPrCILK15zXQIf8TxxAdPGjRtlWUVAe3EWWFzwJi7cEz9NimyO+GMksjniQiHxPBFEi9coLgwSP+/Wl/jJWvwxE92z/fe//5VBobh4rqx8IggV+xCTKJ+ohzLip1+RmRMX6ok/fGIb4uKxiwMAcbxENklkZMsu5hEBrshaivNFXHwn6rTsorSasqHi4rI2bdrI1yzKLoIXcSzKXzglzmtxYZxYLjJO9TkHBVGPYhtl57IIIGq6YE4QzSfEBVgiyBaBs3jPlX0hKCMuZhPvQTGJ81TUn8jAiiYW4qIecX6JzLX4EiSOg2gCIM4LQVxsJjKFYj9iHyIQFj8LC+ILkAiExT5FEFjTF4vajmltRKZUHANxcZL44irKIsok3m/iAqdLJYIr8R4S74u6EtlJ8dklAi9xnERAJn4lEZ8LoqlHWca7NqJuxPkpXpOoo/IXczYW8R4QF4yKbsFE/YnjKz43RPazKuLCTnH+ic9v8X4X75WLz6vyxDbF6xevWRwrcb+mwRVEdlx8oSmbxHur7P0pmvWQC7J3I12i+hBdt4iulcRFE+LiCtEt1MyZM20XZogLL5588kl5oUxoaKjlvffeq/Jir/IXzlR3kZggLo4SXcWIi0fExSD9+/e3XbhU1bYuvrBBXHQhLroSF5HU1v2W6BpHdPck1h02bJh8rVV1vyUuoqiq+63aLowoX9aybnfEbUREhLwgRVzYU/6CMdFVkNineN2iayzRJVT5C9eq2kdRUZF8HeI1tGzZUta/KFtZ90mC6PZJXECiVCpt3W+JehL7F93/VNf9VlXH52KiTI8++qjsqku8JtENmOjiqUxaWpq8kEZciCS6GxLLyuqhjDh248ePl88Xx+Oqq66ydSt18Wv++++/5XqLFy+Wj0+dOiUvYhLnZs+ePS1ff/21rHdxsV35er+YOJ6iW7Xqut9KSEiQF9aVdWm2ZMmSKrvfKu/i86JsG+LCQnHBjXjtNZ2T4kK1Hj16yLoKCgqyTJo0yfY6yi6gWbp0qbz4UbxecXFSWTdlgrgwT1z0I+pZo9HIY3HHHXfYtiF8//33sjunsuXinCsjLpwS73NxMVL57rcuvkiqLsdUPK/8OXjxOSveX6JLsrIuoXr37i1ff32636qp7i9W28VeZZ9ln3/+uezWquwYiHPrl19+sXVBWNvnz44dO+R5KI5Pv3795HOr6g6uprJXVdaL9ysurBOfWeL8FV3oiXKWXbx5cZnEBX5t2rSRZRL7FnVfdvFZdXV1cXdstV1AWx4v9nJ9CvGfvYNpImpeImMiMiiOOiKaqxBthkVWUjQ5aOwuhuxJNAkQvySItpbVtYV0FKIbLpGJExlWUWYici2O/QlERORERF+mov2iaI8ofhIVP6WLn9tdKYh1NuLna9Fum0EskWtiIEtE1IjZP9FmMCEhQV6gJXpsePPNN1m/diTaWouJiFwTmxYQERERkVPi711ERERE5JQYyBIRERGRU3LbNrKi02zRhq25GAyGCkOIEuuE5wnfO/ws4ecr/+Y0H/4dds46EX1Ji3JWx20DWRHENmfXQytWrLjkDuxdDeuEdcLzhO8bfpbw85V/c+xnhRPEJmJ45ZqwaQEREREROSUGskRERETklNy2aQERERFRczObzXCkQVVNJpNd969QKC5p0BgGskRERETNEMCKwVLEwCmOdL3Q8ePH7V0MeHp6onXr1g0KaBnIEhERETWxtLQ0Gah16NBBZiEdQV5eHvz8/OxaBpGdTk5OlvUTHh5e7+e7XSC7aNEiORUXF9u7KEREROQGRLCWk5ODmJgYqNWOE3oplUqoVCp7FwNhYWGIj4+Xt/UN8t3uYq9JkyZh4cKF0Ol09i4KERERuUkgKyaNRmPvojgkUS9ldVRfjvO1gIiIiMgFVRWg6UtNKDWZm2R/GpUSnhr7Z1rri4EsERERkYMTQezlb61Fen71I1Zdiha+Wmx8cqRTBrP1xYwsERERUTMSmVgRxG59ZhR8tI0bihUYjBj83zVyH7UFskajEa+88goWLFgg28qKrriGDx+O8ePH47XXXsOuXbtq3d/y5cvx4osv4uDBg5g5cybefvvtKoeZ7d69Oy6//HL88ssvaEwMZImIiIjsQASxvp72azc7Y8YMFBQUYOvWrQgMDJRdhC1evBhZWVl13obohWHevHnyQvrquhZ78MEHZXCcn5+PxuZ2F3sRERERubu4uDgsWbIE8+fPl0FsWS8G4qL4tm3bymytCEB79eqFbt26VZud7dixo1ynut4YfvjhB9kbwRVXXNEkr4OBLBEREZGb2bNnjwxYQ0JCqlweGxuLKVOmYP/+/bLJwLPPPlvvfZw9exbvvvsu3nzzTTQVBrJEREREVEGnTp3Qr18/eX/w4ME4efIk6uu+++7DW2+9BR8fHzQVtpElIiIicjN9+/bFqVOnkJmZieDg4CqHjS0jLgQTTQ3qS7S9nTp1qrwv2uKKwajGjh2LFStWoLEwkCUiIiKyA9HDgL222b59e1x77bUy0Pz6668REBAg+3H97rvvUFpa2ihlKX/RmNjHsmXL2GsBERERkTMTAxaIvl5FN1lNoYWvVu6jNnPmzMFHH32EgQMHyou1RCArut8aN25cjc/r3bs3/vzzT0RERGDdunW44447kJeXJ5//008/Ye7cubjuuuvQHJiRJSIiImpGon9XMWCBvUf20mg0ePnll+V0sYkTJ9ruiz5g4+PjbY/37dtnuz9ixAgkJSXVuq+7775bTo2NgSwRERFRMxOBpjuMvNXU3C6QFR32ikk0OCYiIiIi5+V23W+Jjn4XLlwInU5n76IQERER0SVwu0CWiIiIiFwDA1kiIiIickpu10aWiIiIyO5K9YCppGm2rfIANBcGNHBlDGSJiIiImjuI/aAnUJDaNNv3CQMePlBrMCtG63rllVewYMECOXqXyWSS/ciOHz8er732Gnbt2lXrrpYvX44XX3wRBw8exMyZM/H2229XWC62M3/+fHn/tttuw6uvvorGxEDWRWX98APylv6B0KefglefPvYuDhEREZURmVgRxP7fYUDr27j1YsgH3utq3UctgeyMGTPk0LFiKNnAwECYzWYsXry4wohctenQoQPmzZsne4TS6/UVlm3YsAE//vgjDhw4IAdcGDp0KIYNGyaHqW0sbCProvJXr0bx/v0o2r7D3kUhIiKiqogg1tOvcSdt3QLjuLg4LFmyRGZLRRArKJVK2btT27ZtZbb2wQcfRK9evdCtW7dqs7MdO3aU64hA9WI///yzHATB29sbWq0WU6ZMkYFtY2IgS0RERORm9uzZIwPWkJCQKpfHxsbKwHP//v2yycCzzz5b730kJiaidevWtscxMTFyXmNiIOsGmVn94cP2LgYRERE5kU6dOqFfv37y/uDBg3Hy5MkGbUehUNjuWywWNDYGsi4s6O67oVCrkfPLYnsXhYiIiBxI3759cerUKWRmZla53NPzQvtacSGYaGpQX9HR0YiPj7c9TkhIkPMaEwNZF6bt2BFe/a3fpoiIiMjBiAuz9HmNOxny67Tr9u3b49prr8XUqVORk5Njy5h+++23Dc6+Xky0t/3mm29QWFgIg8GAr776CrfeeisaE3stICIiImpOop9X0UWW6F2gKfiEWfdRizlz5uCjjz7CwIED5cVaIpAV3W+NGzeuxuf17t0bf/75JyIiIrBu3TrccccdyMvLk8//6aefMHfuXFx33XUYMWIEJk+ejB49esjniSC2tm3XFwNZIiIiouYkusUS/bzaeUAEjUaDl19+WU4Xmzhxou1+9+7dKzQR2Ldvn+2+CFaTkpKq3ccLL7wgp6bCQJaIiIiouYlA001G32pKbCNLRERERE6JgSwREREROSUGskRERETklNyujawYC1hMxcXF9i4KEREREV0CtwtkRZ9mYoqMjLR3UYiIiMhNGUwGlJpKm2TbGpUGWpUW7sDtAlkiIiIiewex4xaPQ0ZxRpNsP0QXgr9v+tstglkGskRERETNSGRiRRC76uZV8NH4NOq2C0oLMOaXMXIftQWyYtjZV155BQsWLJDD0JpMJjkgwvjx4/Haa69h165dddrn4sWL8dJLL8FsNstBEcRgCTExMWgODGSJiIiI7EAEsT4ejRvI1seMGTNQUFCArVu3IjAwUAaiIijNysqq8zb27t2L5557Dv/8848c6UuM8CVGCWsu7LWAiIiIyM3ExcVhyZIlmD9/vgxiBaVSKa8jatu2rczWPvjgg+jVqxe6detWbXb2nXfewWOPPSaDWMHPzw9eXl7N9joYyBIRERG5mT179siANSQkpMrlsbGxmDJlCvbv34+ZM2fi2WefrXK9w4cPIzExEVdccQX69OmD559/XjZRaC4MZN2AMTMTpoJCexeDiIiInESnTp3Qr18/eX/w4ME4efJkleuVlpZi9+7d+Pvvv7F582bZTOGzzz5rtnIykHVxnt26o3DjRqS+/rq9i0JEREQOom/fvjh16hQyMzOrXO7p6Wm7Ly4EE00NqtK6dWvcdNNN0Ol0sknBjTfeiB07dqC58GIvF+c3biyMaWko2mk9qXKLS+GhUkLnobJ30YiIiNya6GHAXtts3749rr32WkydOhVff/01AgICZI8D3333ncyy1tVtt92GpUuX4u6775bPX7Vqlez5oLkwkHUjp9ILMO79jQj102LTU6PsXRwiIiK3JAYsEH29im6ymkKILkTuozZz5szBRx99hIEDB8qeBkQgKoLQcePG1fi83r17yy62xAVet956q7wQTFwQJjK34vmiN4TmwkDWjWQVlqDEZEZKrt7eRSEiInJbon9XMWCBvUf20mg0ePnll+V0sYkTJ9rud+/eHfHx8bbH+/bts90XPR28++67crIHBrJEREREzUwEmu4w8lZT48VeLij7p59gOHYcUCjsXRQiIiKiJsNA1gVlffe9vMjL7+qa27gQEREROTMGsi7KZ+QoKMt1nUFERETkahjIEhEREZFT4sVebqK4xIQ3/jxi72IQERERALPBAEs9+mutD4VGA6XWPS4kYyDrJsRACBkFJfjk9r6Y+eNeexeHiIjIrYPYuNGjYUrPaJLtq1qEoP3q1bUGs2K0rldeeQULFiyQfcCaTCbZD+z48ePx2muvyf5ha/Phhx/i888/l91wiemZZ57BLbfc0oivpmYMZN1IkLcHurfyh0UEtkWl8PeqvbNkIiIialwiEyuC2Pbr1kLp49Oo2zYXFCBuxEhrtreWQFYMXFBQUICtW7ciMDAQZrMZixcvRlZWVp33JwZC2Lx5M/z9/XHmzBk59O2gQYPk0LXNgYGsG7HAjPVnl8Ev6ByGvaXCnNv6YnjHFvYuFhERkVsSQayqkQPZuoqLi8OSJUuQmJgog1hBZFQnTZqEdevWyWztgw8+KINUcf+bb75Bv379Km3nyiuvtN2PiopCWFiYDGibK5DlxV5uxKA8jdm7XwXCvpYB7D1f70RcWr69i0VERETNbM+ePWjbti1CQkKqXB4bG4spU6Zg//79mDlzJp599tlat7l69WpkZ2fjsssuQ3NhIOvKzGbAkC/HTtaXmmGRjQpkalZmY3091XK+vtRk75ISERGRA+nUqZMtAzt48GCcPHmyxvUPHjyIe+65Bz///DN0Ol0zlZKBrGtb91/gv5EwrngOhalxaBPiXWmV6d/tRpcX/samE03T4JyIiIgcT9++fXHq1ClkZmZWudyzXF/04kIw0bygOocPH8Y111yDr776CsOGDUNzYhtZV5aTiO0tb0fpqWNor8nD9MtaYvPGiqsk5xSfvy2yTxmJiIjclLgwy17bbN++Pa699lpMnToVX3/9NQICAuQvuN999x1K69Et2JEjR2QvB6LngjFjxqC5uV0gu2jRIjkVF1sDOFenV/nB2LoDSvduhOblj4BR9i4RERGRexP9vIouskTvAk1B1SJE7qM2c+bMwUcffYSBAwdCrVbLQFZ0vzVuXM1D3Pfu3Rt//vknIiIiMGvWLOTm5uKpp56SkzB79myMHTsWzcHtAllxNZ6YIiMj4S700WGImNgaZ2ILK8zvFOaLw+fy4KN1u9OAiIjIbkT/rqKfV3sPiKDRaPDyyy/L6WITJ0603e/evTvi4+Ntj/ft22e7v2rVKtgTIxh3Yjbj8oNm5PtaL+76efpgeXvnvO12LhgREZF7kYGmm4y+1ZQYyLoJ8XOB8uhpzDwKlKrMwIv2LhERERHRpWH3W27iULQCW7qpsPvycCjO98JFRERE5MwYyLqJAm8F1kzpieG33CwfG0wGexeJiIjILSgUCtuvo1RZWb2U1VN9sGmBC8opKsGC1cfwaJeL+nwrKYTX+heRixa46per8NeNf8FL4yUX7TuTi+t6maDzUNmn0ERERC5KDP0qLqwSfbYGBwc3KGBrCmazGSaTye5BrKgXUT+inuqLgawLyi0uxd7EXKS00qNAXBHpUdaY3AI/sxnpFguy9FnQm/QykB3bLRzvrTqO6CAvPDCinZ1LT0RE5Hqio6ORmJiIrKwsOIri4uJmHYWrOiKIFfXTEAxkXdih5FyctvhgRI8WSC2puEyjUGPWX3djzvjvcMeg1tibmAOjyWyvohIREbk0Dw8POQiByII6ShOD1atXY/To0XYtg8hONyQTW4aBrAszmi3waFeC+/UL4WksRStVC9uyn5LP4s7wEqQkbYN/u+bptJiIiMjdXUrQ1hRUKuduUshA1hUpSqFt8RfeiUlHCPIQ6BkMz6JMjDx3wrZKR4MeGvGN0NQ0nTETERERNTXH+lpAjcKkzIVCWQoPswKnFTkYFjUCv/V7Hvfm5gFaX5HHB6LFYAiO0diciIiIqCEYyLqoPkGj0QXelRdovAGlGpjyN0Tz7v/b+iJun98HnsXb7FFMIiIiogZjIOuiooK8MNg3ED4KDToHdQZa9gIi+wMdxtjW+SqrCE+nJEFfWgRTKYepJSIiIufCQNaF3eURga0xt+GmjjcBQW2Be1cDVzxhWx6l9sHwYj2CLdZGBkv3n8X2U5l2LTMRERFRXTGQdWdTVgD3bwI8/dG1pR9C/bT448BZZBYYYDDat4NkIiIiotowkHVnfhFAeA9511urRq/IAKyMTcVlr63GrB/32rt0RERERDViIEsVpOUb5G1Krp41Q0RERA6NgayLEUPNwlLLgTWbUbRnT/MVioiIiKgJMJB1MV9tPg2lUoHoYK8ql6uDg+EzfDgSbr8Dppwc2/xfsw8i15jcjCUlIiIiujQMZF2QUiGmqgc7UOp0iPzoQyh9fXHq+okwpqfjYQSjxGxEgXoPVEoF2raoov9ZIiIiIgfDQNYNKTQatPtzOcyFhTBmZqKbyhsd8jPQ1bsAJ98YjyfHdrJ3EYmIiIhqxUDWTalDQqBQqawPxv8PUGmBrFP2LhYRERFRnTGQJSCoDaD1ZU0QERGRU2EgS0REREROiYEs2VjEZBH/ExERETk+BrIktVB4YE7OPnx/5HvWCBERETkFtb0LQI5hhrY1irTeSMhLQAsfe5eGiIiIqHbMyJKkVijgbSwFzCb5OKOgBA/9sAfvrDzGGiIiIiKHxIysq0qNBQo3AS061rhawcaNyF+5CkHdOgGxf8OiVMAzLBfJublIydPDbLHgsavYrywRERE5HmZkXdXZPUD0IKDflGpXUYeGIv2dd5Exdy7ysyOBVpdhYeFJzNw0CRNGr8b6J0Y0a5GJiIiI6oMZWVfWbhSgC6x2cczPP8Gs1+Ps40/Ix9d7xaAgPxkq3wjsz01oxoISERER1R8zsm5M6eUFdVCQ7XFM29F4NiMT/ZIPAbnJdi0bERERUW0YyNIFncYBjx8H+t7NWiEiIiKHx0CWiIiIiJwSA1kiIiIickoMZKlGYsTahTvPcOhaIiIicjgMZKlaYX6emD68LZ5cfEAOkEBERETkSBjIUrU0KiUeH8vBEIiIiMgxMZClOvl1TxLMZgtri4iIiByGSwSy2dnZmDp1KqKiouxdFKdVsGkzjNnZlearlQo8MroD3ll5HIfP5dmlbEREREROEcjOmjULMTExUCgUOHToUIVlJ06cwJAhQ9CxY0cMGDAAhw8flvMDAwMxb948dOrEn8EbIuCWW6A/eBA5P/8sH2uVapxQlOLVpbfJ4/DI6I7w8eQgcERERORYHC6Qvfnmm7Fp0ya0bt260rLp06dj2rRpOH78OJ588kmZhaWLhH8Co7kUXvU4tH5jr4JX//7WLgoADOv3EB736Yp1GQdYvUREROSwFBbL+ejFwYis7LJly9C9e3f5OC0tTWZiMzIyoFarZXdQLVu2xLZt2+S6wujRo7F69eoqt/fuu+/KqUxOTg4WL17cTK8G0Ov18PT0bPL9PJb8HL6cr0F0fyMy+1yJs6Ej6vS8oB9+gDGkBfLGXiUfZ+RsxFeFv+HJVtY6e2yTEX1bKDCpvRIeKoVT1YkzYZ2wTniO8H3DzxJ+vvJvzgUiaZmUlITqOM3vxWfOnEFERIQMYgXxk3d0dDQSExNlIHv//ffj6NGj8vaJJ55Au3btKjz/0UcflVOZyMhIjB07ttnKv2LFimbZ3+NfPQcvTy/4+VoQ0b07evSp2z7Prl0Hj9bRCDlfxn2HMoEdv2P0mNFQKVXICz6DN/48grvb9sKozmFOVSfOhHXCOuE5wvcNP0v4+cq/OXXnNIFsWfBaXvlk8qeffmqHErmuQI0vspXA9Uuux7IblmFSvyjM23Ta3sUiIiIictw2stURPRKI1LLRaLQFsSJLK7Ky1DgMp0/DVFAo77f2CsNP2UYk5iWyeomIiMghOU0gGxoaij59+uD777+Xj0X7VtGkoKx9LF0a78GDULhxEzI+/vj8jBD46PNsF4ARERERORqHC2Qfeugh2X5VZF/FxVvt27e3Lfvss8/kJC76evPNN2WXW9Q4/K+/HgGTJsFs0FtnBLUFbv9F5L5ZxUREROSQHK6N7Jw5c+RUFdFP7NatWy9p+4sWLZJTcXHxJW3HLag9Ks3acDwDQ9qFwFOjskuRiIiIiBw2I9vUJk2ahIULF0Kn09m7KA6pJD4epSkpVS67tX8Ulu4/iyX7kpu9XERERERw90CWquc9bChKk5KR+t83bfMsCgU+3PMhXt76Mm4ZGI7urfyxcFcS4tIKWJVERERkVwxkycZ7wAAET7kHRTt2IOs760V1whcHv8Avx39BfG48nriqE/SlJiw7cJY1R0RERHbFQJYq8L/xRviNH4/cpUsR4OGHgcV6DCkxw8tsBvJT0CPSH11b+rHWiIiIyO4YyFLFE0KrhdfAAfK+l9oTX6ak4bPkJGhFN1yGPNYWEREROQwGslQ9nzCgzx1A33+LcdXwW/J6GEwG+Hpq8MWGU1i48wxrj4iIiOyGgSxVT60Frp8DXPchZhaWYvm5Ldh+bjueGNsJV/doiX1JOaw9IiIishu3C2RFH7KTJ09mP7L1NKnYiHDPYHlf56FCuJ9nUxweIiIiojpzu0CW/cg2kEoL5CUBBemNe0CIiIiIGsjtAllqoDt/A0TPBbmJtllnsoqQlnd+SFsiIiKiZsZAluomtDOgvHC6DGkXjFPphXjpj1jWIBEREdkFA1lqkCHtQ3D/FW1xLlePVGZliYiIyA4YyFLlk0Kng+HYMZx7/oVKy/bEr0apPlfe7xkZgNRcPWYu2MtaJCIiombHQJYq8R42DGH/+Q8KNmyoMP/qlkOxsCgByze9Jh/3igrAi9d1Q3GpibVIREREzY6BLFWiUCig7dSx0vx7x81FT6U3jJbKgeuWuAwcSrZmaomIiIiagxpu2I+smIqLi+1dFJdx5FwebvtyOzxUShx//Wp7F4eIiIjchNtlZNmP7KUzWswVH5st8rbEVHE+ERERUVNyu4ws1Y3K3x/GzEycuu56tF26xDY/HGq8fu4fGGK/gcliwoCwoXjm6s7w9dTgP78dZPUSERFRs2EgS1XStm2LmB9/RPzkyRXmP6sIRZFfBN7e9bZ8PCJqLz4a9RHO5libaqTk6hHuz+FriYiIqOm5XdMCqjt1UGCleRq1B7Rpxy7MsLYqgJ9Og7YtvDHi7bXIKSphNRMREVGTYyBL9TP+bbTR+EMBoLVfa9tsH60aq/7vCuhLzTAY2VaWiIiImh4DWaqfgChMCeyJA5bWuMOvM2CqnH19d+Vx3PvNTqRxxC8iIiJqQgxkqf763GG9PfQrkBZbafHPu85g9ZE0HEhiv7JERETUdBjIUv21HgzcvQzoNAEwX2hGoFQA1/WKwLD2IWgVoGPNEhERUZNyu0BWDIYwefJkDohQVxYL8laurPOIYB/+qw++v3cg/HWaSzhKRERERLVzu0CWAyLUnbpFCwRMnozkWQ/LPmWrU2oqxSf7P8Gi44sqzJ+7Lg4TPtyI7aeqfy4RERFRQ7ldIOvqrjhYCmV2TqNsS+HhgfAXnofSxwfxt/4LxoyMSuvEWfSYd2ge5u6bi1e2vmKb3y3CD3sScxCXVoCNJzJgsZzvp4uIiIiokTCQdTF3rzGg6PJB8IpqnDaqCrUabZcvgyk3F2nvvgdzYaFt2ZW+7dBOocWcfXMqPe9/k3oh/s0JuP+Kdvh4bRzu+Xonur7wNx5ftF8uF4Ht8ngznv/9EIpKjI1SViIiInIvDGRdUMH4MVBqGu/QasLCEP7CCyhYuxaFO3fa5rfQeOMGZeVBE8qbdWUHTB3WBuuOpaOoxIQj5/JgMJrw6ML9WHrajO+3J+B4akGjlZWIiIjcBwNZqhP/ayZAHRpacaZPKHB2T43PUykV8PZQVZiXU1SK3/YmY2ZPJUJ8tDwCRERE1CAMZKnhul4P9L+3Xk8Ro36JQFY+PUiMD0ZERETUMAxk6dJofevULZcQFaRDap4ekz/bylonIiKiS6a+9E0QAYrzvRLcv/p+vD/ifXiqPW3VcveQGAR4aXB5hxbywq7rPt5cocrMFgtKTWZoVPxeRURERHXHyMGFHEhqnG63amLKykbhli2wlJTIx928IzG0qBj35ubh+3Op2Jy8GXkleRWeE+jtgXuGtkH7UJ8qtznrx73o+NxfWH88vcnLT0RERK6DGVkXoS814ca5W/ATgCBvjybbT8qLL8JSWorQJx5H8NSpCPfwx6ep1gDU1MBtJmUXy9vErKJGLCkRERG5OgayLsRotkC0RtVpKvYS0JhEECuYi/XWGV7B1lvPAEB/ISNcNgBCWfvYMhEBOnRt6YcQXy0UyKqwrNBglM+7+DlEREREVXG7pgWLFi3C5MmTUVxszQI6i2JjMTKKK4+sZXetBwOPHATu+dM2K8eQg1lrZ6HXt72wIWlDhdVFd1t/Pnw5vp0yoELAGhmow5t/HcWCHYnNWnwiIiJyXm4XyE6aNAkLFy6ETtc4I181l4f+eQgjF47EluQtdiuD95Ah8GjTBtpOnSouCIgGPP1tD+/+627sTduLy8Iuk+X+Pe73Wrf93ISumNwvEgu2J8qA9lhKflO8BCIiInIhbhfIOqv0Ims71Ay9/bKyYU89iXZ//QnPbt1qXC+/NB+fjfkMX1z1Ba5pew2SC5Jr3bZSAUwb3g69ogKw4Xg6/vvXkUYsOREREbkitpF1Atd/vAmntYVQNN01XI1CtMx954p3kK3PRpegLlAqlPBQ1b3QoleDN27oIbOyK2JTmrSsRERE5PwYyDqBk+mFMLeyBoqO7qqYq+q1/q39o3AwORc9IwMqzBcDJ/z7qx3o0tIPT1/duZFLSURERK6AgSw10pmks04fXQZMWw9oq+4z9mKPXXVRe9vzjqbky+lQci4DWSIiIqoS28g6m6zTwJ9PAKmxcCjewcCMnUBmHGCoeKGWyWzCwfSDKCgpqNOmfD2t36+8PVSyS7G0vPNdfRERERGVw4ysszm5Fji6VvTQCox/y27FsBgMKNiwAZ7du0MdFGSd6RdxYYWELUDiNqgUJnxx8As5dQvuhsLSQnQN7orZw2ej0FyI1MJUhHmHVdj2NT1bok2INzzUStwzfydGv7see1+4CipxRRgRERHReQxkqUEyv/4amV98Ad+rxyHyvfcuLFBqgJ/+BZzdKx8+GN4dvfvfj2S1EnP3zZXz8kvy5cAHb6W/BcNiA367/jfE+MfYNiH6l+3eytqd128PDcGA1/85P8ACA1kiIiK6gIEsNcz5Eb4sesOFeUoVcO8qIOsUoFACIZ0QsnQmrsvLxeq2A+QqAdoLF3UVWgptgS0RERFRfTGQpXrzu3ocTFlZUGg0sBiNFRdG9LFOZUK7yJvRrUfj75v+xtmCs3hi/ROsdSIiIrpkvNiL6s3n8ssR9ekn8Bo8qF7Pa+XTCl5qL5SYSnAs+5htfom5RPY9a7aYkVmcKS8OIyIiIqoNA1lqMNGWVR8bi5zfax+Ctnww2y6gHW5bfptt3uPrH8fwn4fLeSMWjsCnBz7lUSEiIqJaMZClBvO79lp4Dx2K7O++r33ltKPA19cgYPe3+Hrc15jaYypGe49GkGcQMoqtw+7GZlq7FBM9GRARERHVxu0C2UWLFmHy5MkoLi62d1GcnsrHB75Xjqp9xZxEIG4VEL8ROLgIKqUKD/V+CNdpB0IpeyMAWuhayNsQXUjFfSisPRXMWLAX+lI2OSAiIiI3DmQnTZqEhQsXQqfT2bso7iF6MHDsT2DNaxXnWywYtu9RoNCajX1m4DNYf8t63Nzx5gqrBfto8dO0Qfg7NgWZhSXNWXIiIiJycG4XyFLjs5hMsJRUE2T2uR0YMB0wVh6dS2MqRPvSEnipPBHtGy2bGSjO9xUr+o3dmbITiXmJGNQ2GOcTs0REREQ2DGTpkqjDwmA4ehRHe/ZCwfr19X7+Fynp2D7iU3QK6lRh/vaU7ZiyYgruXXkvjxARERFViYEsXRJdjx7osGkjfMeMxpnp91e9kk+o9dYruOaNnVoHv4O/YfnJP2RPBp4qTxhMFwZcmL/pNAxGtpMlIiIiKwaydMnUISEI+89/ql+h/73AwweACe/UvKHY3/CvkzvwvXdPfDzqY7wz4sL6r03sjh+2JyL2bB6PGBEREUkMZKnpiQauga0BlRYoKQQyTwLfXg+LaA8rhrItRwWgi9oHvUN7V+jB4PaBreGn40B0REREdAEDWWpU+WvXVr8wpCNgKgU+HQacXo+tPf4LeFu73aoLrVqFp345gITMwsYpLBERETk1BrLUKFTBwfC/4QYkPfAgjOnpVa8U0h64fSFQWgSodcj3jql5o4Z8FOqz8dPPE2Exm/HNlAEoNZlx5BybFxAREREDWdeUdxYoSGvWXSo9PNDytVflfYvZOshBlUK7APeuAaZvqNSs4GIdzCrclpOL1/UnYTKVoE2IN/x0msYuOhERETkpZmRd0dY5gFIFRA2EQ4q8DGjRsdbVNEo17sqrmH311Kjw0tLD2HcmpwkLSERERM6AgawrMpuAMS9bf8p3RhlxwHc3AtvmVlr08b/6ICLAEweSGMgSERG5O14GTvalVAPLHwVu+PTCvDPbLtxXVfyuFerniSBvLYpKTHL0LwWH/CIiInJbzMhSo8v+6UckPfJ/ODXxBpyefAsKd+yofuVbfwAKUoG5g4DdX1e72lexX8MkMs0AooO88OZfR7FgRyKPHhERkRtjIEuNeDYpEfLgA3Ko2uID++F/3XVQenmhaNt2GLOykPrW/yoPYxvRB/i/WGDyt0DPW4Aek6zzPf3lTbDSE09kZuOj/XOQVmS9gO25CV1wS78onM0p5tEjIiJyY2xaQI1G/MzfYtYsOZUxnDopb/NXrkTWV1+hcNtW+FxxRcUnigvTul5vnUqLrbet+gEFKVBYzLjri1F4OzjowupKBdQqBY8cERGRm2MgS45FowO6XGu979cSyE+1d4mIiIjIQbld04JFixZh8uTJKC7mz9LNpWDTJugPH6kwT1yoFfrBh4gbOxalqc3b5y0RERG5BrcLZCdNmoSFCxdCp9PZuyhuIejOO2WTg5yFC+Vj49lzyF60SN73PHUKpQmJMKam1NyrgRg4wWIBvr0eyK9hXSIiInIrbhfIUvPy7NQJ3kOHyPtegwbBb/zVSP/gw7pvwDsYuPcf0QAXKMwAcs40XWGJiIjIqTCQpeY72bRa+N9wY/2f2KovfDx8MCXYC2mG7KYoGhERETkhBrJkdxmffY74W26F/tixatf59bpfUagAzumzmrVsRERE5LjYawE1uYDJk+XFXb6jRlW5vOCff+Rt8Z49silCVcK9w6GxNGkxiYiIyMkwkKUmpwkPR+jDD8v7xQcPWWeWljZsY+aSRiwZEREROTM2LaBmZy4owIkRI2HW6aDw8LDNz12+HCcnXIPcpUurfqIYOOHPJ4Ck3bZZItN7KDkXWYUMcImIiNwNA1lqdha9HqasLCS/9CKU/n7WE9HfH8W7dsOYkYHC7durfqJXMBDYBkjYJLvjMpot+GT9SVzz0SZM+3ZX874IIiIisjs2LSC7sZTLxoa/8Dw8u3RB3rJlKE2tbjQvBf4MbIHOa15Hr1aleHFvdzm3b3QA8vXGZio1EREROQpmZKlZadu1lRd/hTz4gLVv2LIT0dMT2rZtrYMfVGNaz2lYbs7Gxg5DMP7sRxho2oUJPVti5qgOzVR6IiIiciTMyFKzUnp5oeUrL1sfrFiB0Mceg+HYcXgNHFjrc2/pfAt+i/sNJ8IHoGeeBTfkJ8G7W3jTF5qIiIgcEgNZsquAiRPrtf6QiCGYf+R7hAe1wQ3tIoCuYVh7NK3JykdERESOi00LyCGZCwthMVZu9zqr7ywMbDkQepjsUi4iIiJyHAxkyeEUbtqMY5f1Q9LDj9i7KEREROTAGMiSY1EpYTzfa0FpcnK1qxVaTLAcWQqcWt+MhSMiIiJHwjay5FCC7roLmrBw2bQg47PP5MAJqqBAtPnlFyiU1u9dUb5R+ODMOvj6tcAtB34GOr9k72ITERGRHTCQJYei8vFBwE03In/tWpgyM+U8Y0oKYDYD5wPZx/s9jqLSIqSmHbNzaYmIiMie2LSAnI5SoYRKobJ3MYiIiMjOGMiSc7BYqp5fkAq1Ibu5S0NEREQOgIEsOSQxypdHu3bwGTkSSl9fnBg5Cqa8vIor+bYEzu5Fp53P2auYREREZEdsI0sOyaN1a7RbvkzeFwHs8QEDYS4ogMrP78JKgTHAlS/Cc/OXCDQzK0tERORumJElhyeDV4Wi6oXhPaAtSsELRW80d7GIiIjIzhjIknNr1RdHB7wOnaXY3iUhIiKiZsZAloiIiIicktu1kV20aJGciouZwXN2+9L3ITEv0d7FICIiIjtxu4zspEmTsHDhQuh0OnsXhepBFRSE0zfdjNKzZ+Xja9tdixxDDr44+AWUCgVKTWa8s5IDJBAREbkTtwtkXZXRbIRXm/cgelv1qqbLVWfWdtkfcmQvY1qafNw7tDeuan0VjmQegdkvB8E+Hvh+W4K9i0lERETNiIGsCwWyKs9UeKm90MJSzRX+TkwdGAiFquJoXuNixsFb4425Z/6Gt4fbtZIhIiJyewxkXYxC4dqHtDQlBQUbNsBiNCLGPwbXtbtOzhfNC/L0Rtzx5XaYzS6YkiYiIqJKXDvqIZeT/PgTODNtOnJ//73CfA+1Ej/cOxCb4jJgZCBLRETkFhjIknMxGuWN2WCotKh9qI8dCkRERET2wkCWiIiIiJwSA1lyevH6dKxXWTO1RERE5D4YyJJTGxk9El29I/G+R4m9i0JERETNjIEsObUgzyDcEDrQ3sUgIiIiO2AgS85PqQSMBvj8/TD8UGjv0hAREVEzYSBLTsOze3eoAgOhiYysuCC8F+AbDs9DP2KN9jHAzPayRERE7oCBLDmNqLlz0HHrFmg7dqy4QAxkpgtE5gOxCFHkYf2xFPy8M5EDIxAREbk4jutJLueZHzYhA/5oH+qLy1oH2rs4RERE1ESYkSWnVLRtO4xZWRXmWTReSLEEYp32/xCIPDHHbuUjIiKipsdAlpxOwI03QH/kCLK+/bbiAg9vDDJ8DB0M8FZUHvmLiIiIXAsDWXI6vqNHw3vY0GoSrgpYoEBvRVzzF4yIiIiaFQNZchlatRI+WjX+sgzGe5q50GUcsneRiIiIqAnxYi9yGb6eGqx/YgTMlhHIfKcrlCY2LyAiInJlDGTJpQT7aOVtir0LQkRERE2OTQuIiIiIyCkxkCUiIiIip8RAloiIiIicEgNZIiIiInJKDGSJiIiIyCkxkHUyFguHXSUiIiISGMg6CV9Pa09p2UWl9i6KwyjavQuGU6egVqiRXJCM93e/X2F52KHPgcJMu5WPiIiImhYDWScR4KWRt8zHnq+PG29ESXwCTo2fgA5L9uGBXg9g8YnFtvqarbkf3hkHgPiNdjpiRERE1NQYyJJT0vXsiQ7r1iL4vntRvHsPBkcMhslsQkZxhly+XdkXpV5h9i4mERERNSEGsuS0FGo1NNHR8n6ILgTeHt647vfrYDQb5Txmr4mIiFwbA1lyCSKQ/WnCT8gvyZcXxIX7eyIurQCnMwvtXTQiIiJqIgxkySUtuG8QdBoVsgtL7F0UIiIiaiIMZMkleWpUUCjsXQoiIiJqSgxkiYiIiMgpMZAllyYu+Coxmu1dDCIiImoCDGTJ5UxfPR3X/349spQW/LY3GR2f+ws/70y0d7GIiIiokVmHiyJyAWqlGiqFCrtSdkGpUCJNrUJmjvVir/jMInsXj4iIiBoZM7LkMvy1/vhhwg9YMGEBWni1sHdxiIiIqIkxI0supVtwN9v9MD8tbusYDe/SSLuWiYiIiJqGSwSyBoMB06dPh5+fH0wmE+bMmWPvIpED0KpVGNghBCtPqOxdFCIiInKHpgWzZs1CTEwMFAoFDh06VGHZiRMnMGTIEHTs2BEDBgzA4cOH5fzFixfjiiuuwIcffojAwEBs3brVTqUnezAXFMCUk8PKJyIicjMOF8jefPPN2LRpE1q3bl1pmci6Tps2DcePH8eTTz6JqVOnyvkJCQky+BXatm0rH5N70LZvD8Pp0zgzY0bVKxxeAp0xr7mLRURERO7YtGD48OFVzk9LS8OePXuwcuVK+fimm27CjBkzEB8fL4PesuBVPL766qsrPf/dd9+VU5mcnBysWLECzUWv1zd4f0ajEcXFxfJ+UVGhvE1ITMDRctsrNJbY1i0q1uPQ7t3IPGmCI7uUOinP89ZbEfDHHxW2pS/W47hnb3Q68Sei1b7Y5DMOK1Y4/hecxqoTV8I6YX3wHOH7hp8l/Hx1mkC2OmfOnEFERATUamuRRdOD6OhoJCYmyqD2gQcewIEDB1BSUoLBgwdXev6jjz4qpzKRkZEYO3Zss5VfBCcN3Z96ywrodDpkFwJeXt5AJtA6ujVal9teTnEhXlwIWT9eOi/0u+wyoP1oOLJLqZPyCjw9kbZ2LXqV29bbv7yNjsOnQrcmC4H5gWgT1RZjx3aGo2usOnElrBPWB88Rvm/4WcLPV6cPZMuC1/IsFjFuE6DVavHVV1/ZqVREREREZA8O10a2OlFRUUhKSpI/nZcFsSJLK7KyRMaMDJy4YgROXT8RFpNjN6kgIiIiNwtkQ0ND0adPH3z//fe2ngrEBV5lF3mRezNlZ8OYmgrDsWPA+UD2UMYhW9Y+JVePohLrlyAiIiJyDQ4XyD700EOy/arIvo4ePRrt27e3Lfvss8/kJLrfevPNNzFv3jy7lpUc15XRV+KDPR9ggzkfkYE6rD6cijf/OmrvYhEREZErt5EVgxlUN6BBp06d2Ecs1cnTA57GgfQDMOTkoldkAKYHtcWpDGuPD0REROQaHC4j29QWLVqEyZMn27qzIjdgMiA0/zA0Zr29S0JERESunJFtapMmTZKTaL5AbmLdbEwuLUTLoBtxOqMHgn084OepsXepiIiI6BK5XUaW3FCptUlB26yNeOvd2bj3m132LhERERE1Agay5PS8Bg9G6BNPoOVrr1a7Tmz4DdhraofbVP8gu9A6ChoRERE5Nway5PSUHh4InjoFPiNHVl7o3ULepPl2wXpzz+YvHBERETUZBrLkMhRi+GKVCon33gdzUZF1Zq9/Ac8ky4wsERERuRYGsuQyVP7+aPPLIhTt3AljVrZ1pkKBDLMehwtXQa9mrwVERERu3WtBYWEhNm/eLAcs0Ol06NWrF7p27Qpn6n5LTOx+yzV5dukCKJXImj8fqh5mOe+LA19gY/YCtApuBaTbu4RERETU7BnZhIQE3HPPPWjbti1mz56Nf/75B7/88gtuvvlmdOvWDfPnz4czEF1vLVy4UAbh5JpavvIKcn/7DS2TipBenI4cQ46cb4F1uFoiIiJys4zsnXfeiUcffRRffPEF1KItYjnx8fFy6NiPP/4YM2bMaIpyEtVZwE03Iv3jjxHlF433d78v50X5xCDApETfvKO4rmQ5gCtYo0RERO4SyG7YsKHaZTExMfjvf//bWGUiahRTu0/FzL4fy/ufH/gcCTmnkK2NwjVxf+LNv6bAX6fB/Ve0hUKhYI0TERG5y8Vep0+fbvySEDU1pQpZESNQajLj0/UnMfvvozAYre1oiYiIyE0C2f/973+NXxIiIiIiosZuWtC6dWt06tRJ3rdYLDh27Bjmzp1bn/0QOaScolKE+6vsXQwiIiJqqkB2zJgx+PLLL22PH3jggYbsi8hheKiUaOGrxci312HjUyMR4qO1d5GIiIioKZoWvP322xUef/LJJ3BWog/ZyZMnsx9ZN+epUWLz06NgNJtRZDDZuzhERETUVIFsQEAAXAX7kXVvncJ9EeTtgVcndrd3UYiIiKg5L/YS/cgSOTOtWiWbEVzfu5W9i0JERETNGcju27fvUvdH5LC+2HAKA99YjT8PnrN3UYiIiKiput8ickW7E7KRmmfA/jPWIW2JiIjIsTGQJSIiIiLXHqK2rA9ZIme1L20fBsevxBXaUrxp78IQERFR82Zkf/rpp0vfI5GdJOYnosCkx1FlxWFpt57KqNeXtITMQizZlwx9KbvtIiIicppANiwsDN999x3y8/PlY6PRyCwtObXJ/aLw/O+x2ByXiQKDsU7PeWrxATz80z78tCOxyctHREREjdhG9vXXX4ePj48cUKB9+/YIDw/H5s2b67sZakQmswVfbT7NOr1ISXy87f717a7H1O5TcV/kmArrvH5DDznC14M/7MauhCy0beFdaz0aTdbsrdHMpjZEREROFch6e3tDoVDgzz//lEPXbtu2Dc888wychSuO7JWUXYSP18TJ+wp7F8ZBeA/oj3MvvoiCTdYvWWHeYXjkskfQ17dNlevn6Y14e1IvXN4+xDZPNDf450gqYs/mNlu5iYiIqAkDWa1Wi3Pnzsn2srfccgvatGmDwsJCOAtXHdlLrWQHFOVFzJ4Nzy5dYC4oaHCdbj+dhanf7MKd83Y0whEiIiIiu/ZaILz66qvo06ePbFIwcuRImbUquIRggagpWYxGGNPToQoJkb8k1EepyXpRWKnRervmaCrO5uhhYu8dREREzhnIXnnllUhJSbE9PnbsmAxoiRxR6ptvwpSRgbDnnkPQHbfXur5Wo8Ivu5PQKdxXtp0t74Hv98BwPqglIiIi+6vz79Hbt2+vcn6nTp3w6aefyjanhw8fbsyyEV0yEcQKxtTUOq0/c1R7XNGxBT5bfwp7EiqO8MVLu4iIiJw0kH3rrbcwevRozJ8/X2Zhc3NzZWZ23bp1ePLJJzFw4MAKmVoiZ+TrqcHDozsg0FuD91Yft3dxiIiIqDGaFixevBg7d+7EZ599JrvgSkpKkj0Y9OzZEzfddBO2bNkiu+UicnhmI7Zt+R+8241Bj5Du6B0dIJsMtA2xnr+tg71x82VR2HYqy94lJSIiosZqI9u/f385ETmt0M44rVHhvhPfQn38G2xPLcScmbsBr77VPqXEZMaUr3fCaDJDrVRU6D92Z3wWnv/9ECb2aYX7r2jXTC+CiIiIBPbZRO5Fo0PZ5VpGhQIWfRagt/YTuzFpI57c8CRiM2MrPEVka9ccTcOP9w1CywDPCst2xWfjaEo+1h1La7aXQERERA3stYDIVf164lesTlyNcK9wRCsmV1reI9LfLuUiIiKiqjEjSy5L6eFhvaO2fl/THz8O//tfxb/WmS6spNBcuF96frQ3o75Zy0lEREQN43YZWTFErZhcaYhaqlrEW7Nl8FqwZq18rD94COoTieiXDfw4otyK2fFATgKQer5JQfJuIOYueVf0Jds2xFv2ZuCh4vc+IiIipw9k09LSZBdcl19+OYxGI8xmMzzKsl9OMEStmCIjI+1dFGpimogIORVu2FDzigsmA6YSILQF4K0DLBcGPfDzVOPn6YOrfNrKw6k4nVEoLwAjIiKi5lfvFNOvv/6KAQMG4M4775SPY2NjMXHixKYoG1GTUSqU0Kl1uDmiBfIspQ0a8mDH6Sz0iQrAM+O7NEkZiYiIqJED2TfeeAO7d+9GYGCgfNyrVy8kJCTUdzNEduWt8cbSiUtxRq1CrrJub4MP9nyAQv9vAGURLu8Qgm4RfvjXgGgEepVrZ0tERESO27RAqVQiODi4wjxnaVZAVF64dzjq0yjg29hvUepZApW2Hyb1G4rrekXI+b/uSWLFEhEROUNG1tfXF6mpqVAorCHA2rVrbdlZIkdlKSmB/tjRKpcVKJVIOt+zQZl0fSK8YuagwGtZM5WQiIiImjwjO3v2bIwfPx6nT5/GiBEjcOLECfzxxx/13jFRc8pasABKnQ4l/boC8YcvLFCqcV90W+SaiuFrunCRV3LxMah0Z1BszsI/if/gyugrbct8dRrZkwERERE5WSAbExODNWvWYMuWLbBYLBgyZIjsuYDIoZWWwnf8eOQMigHeKRfIKhQyiBXyy3Wv1SbEG7ozMWjlE43nNj2HK2+7EMh+eVc/dA/n4AhERERO17Tgqquugr+/P66++mqZmQ0ICJDziJxBsC4YKoUK3YK71bheiI8WPSPC8NboR5qtbERERNREGVmRdS0pKZF9xorBBEQ2VsjNzUVRUVE9d0vUfHxGjoQh7iR8Rl8JP09PKPxj8MVVX1S7vjizcw25PERERESukpF9/fXX4ePjg4MHD8Lb21veF1OXLl1w++23N20piS6Bz+WXo/W338BvzJg6rf91YRz+t+t/iPGPYb0TERG5QiD74osvymzstGnT5G3ZlJOTg+eff75pS0nUxK436zCisAjPZ2Thcm0Y5lw5B88Neo71TkRE5EoXe33yySdNUxIiO7rRrEPftAx5f3LQYCByOI8HERGRqwWyGRkZePnll7F//37o9Xrb/B07djR22YicRlJ2MTadyMCwDiH2LgoREZHbqHevBVOmTEFkZCRSUlJkk4LQ0FCMHTsWzmLRokWYPHmyvGCNqC6MZiMeWfsISs2lUCkqv2VE8Noh1AcvLDnECiUiInLkQDYxMRFPPfUUPD09ce211+LXX3+Vfco6i0mTJmHhwoXQ6XT2Lgo5gAjvCHgoPRBkqf6toDfp5aAIn2cXI7ykBMg8eWFhfgpCd7yFRztlNk+BiYiIqOGBrIeHh7zVarXIysqCWq1GUhLHmifn9Ov1v2LjrRsRU4dWNj0LznfJVXo+m39kGfBOJ2DjO4je/x7O5erxzspjTVxiIiIianAb2U6dOskA9o477sCgQYPk4Ah9+vSp72aIHIJWpQVUVSwwGoADPyMmtAvu63EffD18oUt8quI68RtxuOMovKspxlUFRZh5ZXvMWROHx67q1FzFJyIicmv1CmRNJhMiIiIQFBSEhx9+GP369UN2drYc5YvIpcT+BiydCU2Lzpj10HbrvN/KBbLH/gKO/42tUZ2xPT8OZoUW/+kaJgNZIiIicsBAVqVSVeidYOjQoU1RJiL7s5gr3paXfw44vhYI7Qa0HggcOtzsxSMiIqIGtJEVF3jNnj0baWlpcmjasonIpR38BTAbAbUW2PIhELcaaD0E8PCxd8mIiIjcVr3byD7++OPy9plnnoFCoYDFYpG3otkBkaNTqNUoTUpG/B13wCMqGi1ffw0KpRIYMA0IiAYM+cCRpUBO4oUnFWcDi6cC/e+FUn8Yc6DCf8+cRngVXXERERFR86n3X+Lyw9OK4LXslsgZeA0ahIi3ZsP/mmuQ+9tvsJSWWhf0nATcPA/mMbNRWBAF074lgIevtaut726wrjPmFbw5fDZSvAKwd/BUoO9dFbZdWJoPtPoQI7+/FY/8vAv6Ur4viIiImhJTSuRWxK8HflddBd9qBvHI+HYxEr87ibT9fkCrPsC/fgLO7rUt79miJ1r4tgLajQK0FZsVZBkyofQ8gwxTLH7fn4jkHA66QURE5FBNC4hcmbnYOuyyIUeDxJ+S4ZW5A1UNOptnyMPWs1thMBls8+LzTjRjSYmIiIiBLLk9Y3Y2TNnZ0LZta6uL4kwPILMYpX+vQ8iQylX01s63UGIusfZDC2A/DNh74FUYCzpC7XPc7euUiIioObBpAbm9xKlTcWr8BBTWY6hlEcQOazUMvVv0xki/jihRAKFeEShO/pdc7o98t69XIiIihw5kc3NzcejQocYrDVEzy/zyS5iysuV9U+75IWjraFDLQfhy7JcYE9BZPlaYS+EJa1OD1R5PQlWY2gQlJiIiogYHsuPGjUNOTg4KCgrQq1cvXHPNNXjhhRfquxkiu1IFBCDsP/9BxsdzYExJubSN+UdZt1mYAg2sPRV4KQzwPfar6OajMYpLREREjRHIpqamIiAgAH/++Seuv/56nDhxAr///nt9N0Nk994Lgu66E1DX0kxcpQF63w4MfABQ66zPhULeKsv6kfVvZZ1vscDHUyXvf2+8EoE73wYStzbp6yAiInJn9Q5kS8/3u7lhwwaMGTMGGo1GBgVELkkEqxPnAle/CYiBEwD8Z+B/8PSAp3FD+xsqrgpg2YzL5f3PFDfD5BVe9RC3REREZJ9eC7p37y6bFxw9ehRvvfWWHJ6WgSy5k05BneRUFY3K+qVOvCeyikrgbTCCg9gSERE5SCArgtc9e/bI9rFeXl5ITk7GY489BmexaNEiORUXs7N6ajrvTO6JkkVmZGQWoRsrmoiIyDGaFkycOFFObdq0kY9btWqF9957D85i0qRJWLhwIXQ6a3tHopoY09OR/dNP9a6k/q2DwBY3REREDpKRNRqNKCkpgdlsltlMi8Vi64JLNC8gcjW6Pn2g69kTKa++BsOpUwh7+mkozreTrVZJQXMVj4iIyO3VOSP7+uuvw8fHBwcPHoS3t7e8L6YuXbrg9ttvd/uKJNej0GoR+vRTCH/pRWR/+x0seuvwtdUym4AvrrTeV3nIm2+2xKP7iyuw9lhaM5SYiIjIvdQ5kH3xxRdlNnbatGnytmwSfco+//zzTVtKoiai9PSUtwoPa+B5MXHRlt+YMRXmpc+Zg5PjJ6Boz56LVgYQah0cAWrr0LWJ2UUoMBhxIpUjfREREdn9Yq9PPvlEBrApKSmyuUGZ6Ojoxi4bUZOLWfgzzLm5SH1zNjLnfQVDXBwUXl6w1NBcpnDzFpScOoXi/Qfg1bfvhQVB7YAJPwA/DuWRIyIicsRA9ptvvsHMmTOhVquhUqlsWau0NP50Ss5He/6iRUF/6BCC/n0XlH5+yPjoY7uWi4iIiJogkH3llVewY8cOdO58/idUIheiiYiAWW+wdzGIiIioKbrfatGiBYNYcjmq4GDrbZD1tj56teiFAeEDML7teNu8hLyERi0fERERNUJG9sYbb8THH3+M2267DZ7nL5QRxOAIRM6q1dv/gzEzE5pWrZD5+Rf1em60XzTmjZ0n75eaSmVge/vy2/GVteUNEREROUpG9umnn8asWbMQEhICX19f2QWXuCVyZkqdDh6RkbK9tyogQM4ru60PjUqD78d/D7VSDbNaCZ1GheggfskjIiJyiEC2fNdbJpPJdkvkKgImT0Lb5csQ8cbrNa6Xu3Qpjg8dhqxvvqlyeZC3B+bf3R89I/2bqKRERETurd6BrCB6KNi4cWOFEb+IXIXIymrbtYOyluYyhiNHYMrMRPGBg7Vu82yOHiVGcyOWkoiIiOodyP76668YMGAA7rzzTvk4NjYWEydOZE2Syzv3/AtIuOceGbzWxd8aC0wWM3pHBeCnnYmYuy6uyctIRETkTuodyL7xxhvYvXs3AgMD5eNevXohIYFXaJMLU4ghu4C85ctRtHUbSupwvk/rOQ3zPS3YkxuHey9vi9sHtka+/sIAIkRERGSHQFapVCL4fFdFZTyqGd6TyBWo/P0R+fFHiHjnbdmrQV3c1/M+hLAlARERkWMFsqKHgtTUVNmOUFi7dq0tO0vkqnxHj4b/hAm27CwRERE5YT+ys2fPxvjx43H69GmMGDECJ06cwB9//NE0pSNyFRYzvI3ZgMkIqOr9tiMiIqIq1Psvatu2bbFmzRps2bIFFosFQ4YMQUAD+tskcieDUn/CmKSPAK8ZwNiau/UiIiKiJmpa0KFDB9x7770yiL366qsZxJJb0kRHW2+joqpfSaEENr4LpB6Gb2m6nLVm5z68v/p4cxWTiIjIpdU7kE1MTMSECRNkE4OoqCg888wzOH6cf5jJPSjPj2IX/tyz6Hw4FsFTpyB/1Sqkvfd+5ZV9wwAPbyA11jar0GDElri6dd9FREREjdy0wNvbG3fffbecTp48iTfffBNdunTh6F7kFqK/+BzGtDR4du0qHwdMngxjRiaKduyovLJSbQ1kxShfXtaePXw9Nc1bYCIiIhfWoKtOxGheS5cuxfz587Fjxw488MADjV8yIgekDgmRUxmFUgmPqMiqA9lyOob5AqeB9qE+AEd0JiIisk8gO2vWLCxcuBB9+vSRWdnFixezH1kiIiIicvxANiwsTI7s1aqOHcMTEREREdn1Yq+jR4/K22effVYGs+Vt3rwZzmLRokWYPHkyiouL7V0UchdntgNGvb1LQURE5L6B7G233Wa7P2DAgArLZs6cCWcxadIk2TRCp9PZuyjkDmIuB2J/BXbPt83KKDDgti+24ZlfD9q1aERERG4TyIp+Y6u6X9VjIjqv67VAmyvkyF5lTmUUYsvJTPy+N5nVRERE1ByBrKLcGPPl71f1mIisSk2lyLCUsjqIiIjsebGXXq/HkSNHZPa1/P2yZeQoLICF/Ts5imc3P4uM4gzM9vbC+MIiexeHiIjIPQPZoqIijB8/3va4/H1mZB1DpzMWWPSFQH4O4MdeJRyBCGKFNJXK3kUhIiJy30A2Pj6+aUtCl0RjKsXLP5igbWWC5sFfgdAurFEHpYMej6kX4ZyiJYBx9i4OERGRe43sRY5HtFJWWoCAYQaofP3sXRyqQW/lSdyr/gtFFi2A91hXRERETX2xFxFdIv8oViEREVEjYkaWqDlc+QKQXwQc2cb6JiIiaiTMyBI1B5WHuCqSdU1ERNSIGMgSNQJTdhZKEhJqXknrh5bn/sFczQcosHiy3omIiC4RA1miS+TZo4fsUzlp5qyaV+z7b2wc9i3uKXkSU0qeYL0TERFdIgayRJdI27Ytwp99FpaSklrebUrkBPbEPkt7ZMCf9U5ERHSJGMgSERERkVNiIEtkRyVGM+ufiIiogRjIEjWigvXrUbh9R63r6TTWIWuHzV4DfamJx4CIiKgB2I8sUSMRvRacmX6/7Gar0+5dNa4b5O0BXakSafkGlJjM8Dwf2BIREVHdMSNL1FgsFtutxXz+/nl/nv4ToxaOwpb03y7MZL+yREREl4QZWaJmcCTriLw943EUQBvWORERUSNgRpaoESi01gEOFJ6egFqNM/dPh1bPC7mIiIiaEjOyRI3Aq28ftP7uW6iCQ2ApMSB+0mT4XR4KcAAvIiKiJsNAlqiRePXvf+GBihdvERERNTU2LSCyF7MRPRSnWP9EREQNxECWyA4yFEEwRQ3Cbx4vACUFPAZEREQNwECWyA6KFTrob/wWaoUZMPOiMCIiooZgIEvUjMq6jlWyD1kiIqJLxou9iJpRS39PvHVTT3Ru6StGTmDdExERXQIGskTNSGRiJ/ePQqm5FEX5uax7IiKiS8BAlqiJBHsGA0iVt5n6TNv807mnMemPSYjyjkS5AWuJiIionthGlqiJvDTkZWy4ZQP6h5frXxZAelE6DCYDkgqTWfdERESXgBlZoiaiVWmh9Qxk/RIRETURZmSJmlDGZ59j4ivr0TGp6gu7TBYFdH9MBwzsS5aIiKi+GMgSNaGCjRsQlJyPTslVB7KTS16AOn49kMdmBkRERPXFQJbIjnZbOgFKDY8BERFRA7CNLJEDWHbgHNZkFmOQJ/uWJSKihvt5ZyK2n87Ck2M7I9zf0+WrkhlZoiZiOBlnux+abYHGWH2Q+svuRPy25wyOZDOQJSKihvti42n8uicZG0+ku0U1MpAlagLew4Yi+f8ehf5QLFLbBeLyWAtu2GKudv3Xi1/HCe1diNIf4/EgIiKqIwayRE0g6uOPoYmIgEWvx5ERMVjTSwHPkurXb2VJgVphRoAxjceDiIgu2eFzeTAYTS5fk04fyGZnZ2Pq1KmIioqyd1GIqqRgvRARUTMa3rEFFu9OwndbE1y+3u0SyM6aNQsxMTFQKBQ4dOhQhWUnTpzAkCFD0LFjRwwYMACHDx+ucVuBgYGYN28eOnXq1MSlJmqYq2LGYkDLAega3JVVSERETe7eYW1wZZcwGIzVN2lzFXYJZG+++WZs2rQJrVu3rrRs+vTpmDZtGo4fP44nn3xSZluFlJQUjBgxosL0yCOP2KH0RPUT7BmMzoGdoVPrWHVERETO3v3W8OHDq5yflpaGPXv2YOXKlfLxTTfdhBkzZiA+Pl5mcNetW9fMJSVqON9Ro1C0Ywc8u3WF/qJfHsorMHvAF4UwWNifLBERkdP2I3vmzBlERERArbYWSzQ9iI6ORmJiogxkq3P//ffj6NGj8vaJJ55Au3btKq3z7rvvyqlMTk4OVqxYgeai1+sbvD+j0Yji4mJ5v6ioUN4mJCbg6PntpRdbYDZbfz4oNRuxefMWFHgnwdFdSp04hT695XT8xAkEJMQjqzQbKSnAztydcrHJZG2Ef03R87g1uhBD0hbAZDS5dp00gMufJ/XE+mCd8Dzhe6cmhQVG7N69C2dTLDDnKLCi5KRLf544VCBbFryWZ7HU3q/mp59+Wus6jz76qJzKREZGYuzYsWgu4kRp6P7UW1ZAp9MhuxDw8vIGMoHW0a3R+vz2EjILodz+t7yvUaoxdOgQIKwbHN2l1ImzSd2zF4WZhQgPD0f/jv3x6cpPoVKp5LIESzjG3DQchi8XQqVWuU2d1JU7nSd1wfpgnfA84XunJv+LXY/LLuuKxL3JaB/qg7Ej27v054lDBbKi54GkpCSZgRRZWRHEiiytyMoSOTulQoXNZzfjSNYRqBUO9dYjIiInZjJbk34qpfv1k+NQf01DQ0PRp08ffP/997j77ruxePFi2aSgpmYFRM6iY2BHvDb0LlhgkYHs4+sft3eRiIjIyWUVlmDUO+vgqVZh3RMj4G7sEsg+9NBDWLJkieyJYPTo0fDx8UFcnHU4z88++0wGsW+88Qb8/PzwzTff2KOIRI1OpVBiVPQoef907mnWMBERXbK84lLkFJWKq2SgL3X9ARAcIpCdM2eOnKoi+oPdunVrs5eJiIiIiJyLQzUtaA6LFi2SU1kvAERERETknJx+iNr6mjRpEhYuXCh7ASAiIiIi5+V2gSwRERERuQYGskRERETklBjIEhEREZFTYiBLRERERE6JgSyRHYgBEQwmAzxbLhRjsvAYEBERNYDbBbKi663Jkyez+y1qdkW7duHk1eORPncuovyi8N9h70ATsAdQio6siYiIqL7cLpBl91tkL/rYWJScPo3CLVvk435hA3gwiIiILoHbBbJEREREriijoAQGo3s1V3O7kb2IiIiIXNENczdDq1YhKsgL7oIZWSIiIiIXkK834od7B6JNiDfcBQNZIiIiInJKDGSJiIiIyCkxkCUiIiIip8RAloiIiIicktv1WiAGRBBTcXGxvYtCRERERJfA7TKyHBCBHIUpPh5h2RZ7F4OIiMhpuV0gS+QISs+dQ+4tt+OdL03QGkvsXRwiIpeVXpSOB1Y/gGX5y+xdFGoCbte0gMgRmIv1gMkEDwAqi9nexSEicllHs45iU/Im6BQ6exeFmgAzskREREROzkNtDek8Ne4V2jEjS0REROTkQezymcOgLzWjdbD7jOolMJAlsjOFqgCpRWcRYO+CEBGR0+oQ5gt35F75ZyIH5N36CzywfhJOa0z2LgoREZFTYSBLZG9KPSywoEDJrriIiIjqw+2aFnBABCIiIiLX4HYZWQ6IQPagCgqSt0p//2rXUSsU2HzOgjVHU5uxZERE5KzOZBXhxk+2IMhLdOZYUaCXB95bdRwLtifClbldRpbIHoLvnQrfq8ageM8e5Pz2W5XrRATo0FIPHE8twKjOYc1eRiIiV2OxWJCY77qB3NmcYmhUCvz58OWVlj11dScUGow4lpIHV+Z2GVkie1CoVNC2aQOFR+VvzWWUCgU8+I4kImo0qxJW4Z1d76Bni54uW6salRJB3pX/tmjVKgR4a+Dq+GeTiIiIXFJhaSH6hfXDC4NesHdRqIkwkCUiIiIip8RAloiIiIicEgNZIiIiIidjMJqw8nDtvdzEns3DyfQCuCoGskQOZHTpGvgVue4VtkREzeHv+L8x8feJ+GjvR9CqtZWWv77tdbn8hiU34ED6gfptPPMk8PuDwNHlsKeVsalYvCcJU4e1qXad8d1bosBgxNsrjsFVMZAlchSX/RvelkK0TV9j75IQETm1XSm70CGwA14d+ipeHPxipeXrk9bjhg43wEfjU/9A9vASYN8PwLZPYE9iLMh2LXxwz9DqA9leUQGY3C8KFhceOJKBLFEzM6amoXjv3soLWnTEGWUUjwcRUSOI9I3E0FZDEaILqXK56JIrWBfMunZybjcgAoeoJXvy6t8fmogInHv2WR4IIiKiS+R2GVkOUUv2pAkPR8gDD/AgEBERNQK3C2SJiIiI6qUwA0jYAphNrDgHw0CWyMEUlpgQl1YAfSk/MImIGttdf92FtKI0eaFXnf16HzD/amDvd3Y/ICazRf6NMPBvhMRAlsiB6DTA3sRsXP3BBry2/LC9i0NE5HIOph/EvLHzZK8GdVZSWPHWjj7fcAqj312PV5c1zt8Ii8kEU4H9X1dDMZAlciARXgo8flUnPDWuM7ILS+1dHCIilxTmFQZnlV1UIm/z9MZG2V7KSy/jeL9+yF26FM6IgSwRERGRmypNTq5w62wYyBIRERGRU2IgS0RERHQpRI8GJ9eyDu3A7QZEcEUWiwVK70P2LgYREZHDMlqM2JC0ofE3rM8F5o+3Dhr78H4gMKbx90HVYkbWBaQWn4M67Bd539uVB1QmIiJqgCjfKHT37I6H/nlIBrSNymK2BrGCqZG3bSclZ87AlJtbYZ6poAAl8fFwNAxkXYBFvIks1kOpsndhiIiIHIyXxgvX+11v72I4hcLtO3ByzFVIuPueCvOTHpqBk+OuRv7q1XAkbFpA5KCOnMvD0DfXoNRkRpeWfvhmygB7F4mIiFycKSfHepudXXH++cfGi+bbGzOyRA7qVEYhfD3VeOm6bth2KtPexSEiInI4bpeRXbRokZyKi4vtXRSiWvlo1TIbS0RERJW5XSA7adIkOUVGRtq7KESVmJQewNaP0bqtD9opzPhf5nMI2jACwFWsLSIiezu8FMiMA1QeQN5Z4PMRgI/zjhLmCtwukCVyZMdi7kRkwW9ombMb3RRBaGM8hdJ4cRUsA1kioqaQpc+C2WKGUlGH1pZntgFdrgNaDwX+fooHxAGwjSyRAzGqfYCA1vYuBhGRW2gf0B7zDs3Dd4e/q/uTAlsDnv5NWSyqB2ZkiYiIyC3N6DMDuYZcmZUl58SMLBERERE5JQayREREROSUGMgSEREROQiLxYK/Dp7DnkTHGnjAUTGQJXJA7YoO4OrAJHsXg4iImtmuhGw88MMe3DVvB+u+DhjIEjmay+6GztsPVxf+bu+SEBFRVTqOBQbPAEY+1+j1YzRZrLdmM+u+DhjIEtlZjwTrh5ZNSAeg8wR7FYeIiGrjFQSMfR3ocq1L11XB5s0o3LYNjoyBLJG9eHhgXQ8FHv3NjPAsazC7qXATxvwyBn/lx/G4EBHV0f2r7scNS27A0xuflp+hS08uhbfGu07PFev9fOxn+bxP9n3COj/PmJKCM1PvReLd98CY7bjtddmPLJGdKFRKzL1GhUFHjVCd/wUpoTQBKfoUHNCE4GoeGSKiOtl6bqscnSsuJw4z+8zEgPAB6BbSrU7PfaD3AxgRNQIbkjZgd9pu1nhVTCY4KmZkiYiIyGW08W+D3qG9oVFq6rS+VqWV60f7RTd52ajxMZAlIiIiIqfEQJaIiIiInBIDWSJnce4AcIb9ChIRObS8s8DxlYCp1N4lcQsMZImcgBYlwBejgHljgMyT9i4OERFV5/cHgAWTgN1fs46agdv1WrBo0SI5FRcX27soRHWmhBkwn/92byphzREROSqj4fyt/pI3VWAwwmS2QKtWotBghL9Og6ISI5qSOT8PzsTtAtlJkybJKTIy0t5FISIiIqpSidGM4W+tRXGJCS0DPHEqvRDdIvwQezYP0UFeSMwqavSay1uxEueeex6a6GiUJiY6xZFh0wIiB2Qwm/CntxfOqN3uuyYREYnMqAXIKixBcalJBrGCCGKfGNsJdw1u3SR1ZExLg8+VVyLizTed5hjwrySR3ShwbdtroVIuRaBnYIUli/OOYlFoCPoX6/FZ43/pJiJyOePbjEeWPgvdgus2EIKzUioUTbsDxfnJSTCQJbKjNy5/A0dVK6BT6yrMN8M6ZK3RiT5MiIjs6eG+DyPCJ4IHwc2waQEREREROSUGskRERETklBjIEhEREZFTYiBL5NDYSJaIyDkogD8eBgoz7F0Qt8JAlsiRBbW1dwmIiKgu/r0USD8KZJ5gfTUjBrJOpMdpM1r/k4nSQpW9i0LNRe3JuiYicgYRfQGtDxyZxWRCxiefIHPePFjMZrgCdr/lRG7YakHLhDzkGnUIsXdhiIiIyKmUnj2L9A8+lPf9b7yx2vV03bqhxcOzoAoMRMpLL8ORMSNLRERERDYKDw+EPPAAAmoIdh0FA1kichm5hlyUmErsXQwiImomDGQd1NaTmTiemm/vYpCDKyox4p8jqcgsMNRpfaPJjLVH03A2pxiuZnPyZgz7aRgeXvuwvYtCRA5IAw2CPYPhr/WHr4cvHJHZbMHB5JxG325avl5+7u9NzIarYSDrgMSJ9q8vtuHmT7bYuyjk4D5ddxJTv9mFV5cdrtP6C3cl4Z6vd+L/ft4HV5NalCpvUwpT7F0UInJAWqUWqyatwtrJa2Uw64hWHk7Be6tOYHSX0ErLLu8Qgi4t/dA/JhCXtQ6U90d1rrzexcS6afkGjHl3PR7+yfU++3mxlwMyGM0Vboka61wxGE08t4jIbWmUGjgy8VnevZUf/jO+C1YfSauwbPrwdhjW4cKl3n89fHmdttkrKgDPXN0FDy3YA8fMQ18aZmSJiIiIyCkxkCUiIiIip8SmBUTuIP0YcHgJfEoG27skRETOJW41kJts71JQNRjIErmDje8CB35C96hbAFxv79IQETmPZf8HtOwJjHkZiPsH6Ck+R88LagMMngFofQGNlz1L6bYYyDopi8mMgnXr4Nm5s72LQo2g3aliqLfuQ/tjOVCUmBHbWgGDh4J1S0TkCIbMBFoPAXrcXHG+WguMfb3Rd2feswvReSlI9AtHczGcOg2lTlflssLNm2EuKIAjYhtZJ5WzLRFJ9z+As089be+iUD15tGkDXZ8+8Bs7Vj4Wt4N25MFz3q8YviYJDy4346bN7LGCiMgdRZ47idKHH8DbG+c02z69Bg6E4cQJFB84AJ9h5XpDUKvhe9VVyJz/NdQtWsCjXTs4GmZknZT5fDdKFkPdOsInx6EJC0XMjwtsjyP++wam/XYAzw16Dl9tmIvuy3dBY7RrEYmIyE7UxlJ5qzE33x8C/+uuRcBNN1War1AqEfnhB7bHiffeB0fDjCwREREROSW3y8guWrRITsXFrjdEJxEREZE7cbuM7KRJk7Bw4ULoqmnQTERERETOwe0CWSJHdTTzKCywVLnMok3GMQ/HHlqRiKiuUgpTsCV5C0xm6/Ue9WWxWLAzZSc2JW/CiewTjVbxOfocnC0422jbo6bHQJbIAQyPHI45++bgoP5gpWXnlIA5+jPcGhGOQgW75CIi5/fE+icwffV0LDu1rEHP35GyA9NXTcfsHbNx67JbYbZcek8vXYO7orC0EI+sfeSSt0XNh4EskQN4sv+T6B7SHSZUzk5Yr18FjAoFjIxjicgFGEyGCrcNeX6Mfwzmj5uPEnNJo5SpY2BHPD/o+QaXieyDgSwREREROSUGskRERETklNyu+y1ns3DXGRjOD35QXnJ2McSlP+dy9dh2KtMuZSP7+HFnImI6h2LryQykF1T8CcxstuC7bQnILDDA38sDdw+JgUrZNO0R9p3JwZojqbh9UGuE+Xk2yT6IiNzBmawiLBJ/700c1bG+GMg6MIPRjCd/OYCpw9pge2nF5Ll3bhxKoEJqbiH81r8EZQRPflel1IsvKsG2x213vIzcbZ7oDmCFcRLaKVSYfm4OsPc2nIm+AS/9EYtb+0fhk/UnMbR9MDqH+zVJud5bdRzrj6dDqVTgkdEdm2QfRETNypAP5J0FfrwNCIwB1FqgIF0McVX/be39AUjYCvS9C+g0rsZVv9kSjy83nZb3J6kaWnj3xKYFTmDWqA4on1NL8umJFATJ+zoYcJVqN1RgIOuStL7I7znF9rBo/Fxs9xyKTebuCFNkY5RyH65QHkDvws3A7m/kOmqlAv+9sSd0Gn4aEhHVS2YcUFoERPUHts0BNr0LjPwPEDmgftsZ+RzQT3x2W4CDC3kQmhADWSdQ/M9qXLE+G2qTtY/REpUXzlha2LtY1BwUChRFDZd3rzhgRtGONCzRXI1vTWNxytKSx4CIqNE/d1VA339feNzlGkBZz3ApqA0wcBoQNRCOKmfRL6I9GpwdA1knkPPS85jwdyY6J9m7JGQv2hILHlpuRsmHnyE8O4UHgoiIGsSkUKLFI48g/aOPoDnn/H9PGMgSERERuQkLgJD7p0Pp5QVXwECWiIiIiJwSA1kiIiIickoMZImIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickoMZImIiIjIKantXQCqndFsBAcbdU/nCs/hj3MrK8wr9twGlbkj1vrmIjgnDAqTxm7lIyJqartSdmFn6k55/4rIK7A+aT3ic+NZ8SQxkHUCJSYDdPYuBDWbNv5toLYo8b/hD2HbuW3IzU3HtJxcAD5yebFuDbxarMVaAL3NaeiQ3YZHh4hc1v92/Q8+Gh+kFaVh7r65iPaNRp/QPpjSfYq9i0YOgIEskYMJ8gxCt8AuCG8zDuPajMOZuIMI3LIAZ84HskRE7ua2Lrdhc/JmxOfFo29YX7w69FU5P6M4w95FIztjG1kiIiIickoMZImIiIjIKTGQJSIiIiKnxDayTkoPD3kbqchAiaVcnwYmg/0KRQ7lfbyFtj+kATEDgSN/ABpvexeJiOjSHVwMlHoDI56u19OUphLg8xFA3lnAwwf49x+Af6tqVlYDak/AbATU9bjcWutX8ZaaHANZJ7Xf3BYDcRje0CPN4o9SGcyaGciSTT8cgUd+IXAwERj+JGAsBs6ksYaIyLnJz7Id9X6ayqwHzu4F7vkbWHgXkHum+kDW0w94cBtgNgF+Leu+kzsWA/nngPCe9S4fNQwDWQekyM/DMzu+Rap3EL7qdk2FZRaFBbpDieiptT425KnhEWsBwhX2KSw1CcOJE0icPh3e/ftDv20z0uMCL22Dga2BjBONVTwiIufVqi+gPv9HtCZBDeja0CfUOjVATO453HN4OSJQv19WzXo9Ul58CQpPT4Q//xwU6rqFdgqlEoGLFkGfnQ2/a6+Fs2IbWQekjD+J4WcP4LqTm2zzVKM1+Hu0CQk3F8Dkp8OglMM43bIlAtsXQhPHw+hqirZuQ+H6DUh7+x0YD8TCK0qP/05SoqgOn71EROR8Lks7hkB9PvZ0Gwb1C9buxerCmJqK3CVLkPPzzzAXFNT5eZEff4Si/v3Q4rFH4TdhPJwVIyAnoQhV4lB3C0r9zSgN9ZfzTAolfFqyTayrU+h08G1fhL3t+XYlInJlZ31CsKvnCCg6dWnyfXlddhkKhg5F4KRJUHpYr7txRvzLSEREREROiYGsgzEYTdgVn13jOoUGU7OVh5xHWoEBO05n1Wldi8WCn3cmYu2xNKw6nIpfdifJeZfKbLZg4a4zWHM0tcb1Sk1mfL8tAVtPZjZ4XweTcjF/82kUGoz1el5uUSnmbTqNYyn5Na6XkquX6yVmFsHd7E3MxtebT6O45MJnTYnRjO+2JWD7qQvHbMvJDHkcxfEk98ZzoXomswXfbInH7oQLn88bjqfjxx2JOJScKz9nDiaLYcipIXixl4NZEZsqT/ARNaxjPP9HI8BLI28VCkCp5MVe7u5MVhGe+OUANKraz4X9SbnYn3SwwrxuEX7o0vLSuowRH8ZP/nJA3o9/c0K1660/lo7nfj+EFr5a7Hx2dIP29dySQ9h/JgcqpQJ3DY6p8/O+356A/604hss7hOC7qQOrXe/T9Sfx9ZZ4+YfmvVt6w5088+tBHE3Jh5eHGpP7R8l5a46m4fnfD6Glvye2PnOlnDfrx73IKChBRIAnRnUOs3OpyZ54LlTvXK4eL+6JRdsQb6x53PrX/f7vd6Po/BfFnpH+iPDXYUKPlsg/kI5/DYgGLI0b2PaPCcS1vSIQVewJrIZLYSDrYERWLDKw5j7rgn2sbVnahFj7BVUqFPKPOdGlnXuXXn+Weq53Sfs8/+T6bsNSz+c1Rqba2ZS9ZEuFI1q53mzruV8V0UV4LlSv7H1U4d1U7sGUoW0wsU8rZM7bj+LcQET2jUTc7sYNZEP9PPHRv/og/sBx1PxblPNh0wIiIiIickoMZImIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickpO32vB2rVr8f3330Ov1yMwMBAff/yxvYtERERERK6akZ01axZiYmKgUChw6NChCstOnDiBIUOGoGPHjhgwYAAOHz5c47ZGjhyJefPm4YcffkBCQgLy812tYwkiIiIicphA9uabb8amTZvQunXrSsumT5+OadOm4fjx43jyyScxdepUOT8lJQUjRoyoMD3yyCO25y1btgxdu3aFr69vs74WIiIiInKjpgXDhw+vcn5aWhr27NmDlStXysc33XQTZsyYgfj4eJnBXbduXZXP+/LLL5GUlITZs2dXu893331XTmVycnKwYsUKNBfR9KEu+zuQaq4yq1xsMFy4X1wMTzHUZk4ugrysHbabzReGiNy8eQsKvJPg6OpaJ+4iK8s6fGF2djYiys03lDv2dR0mVtTrINHj9vlxMg4eOgTv4rPIziuu9nlbtm7BGZ+6D6yRkWEdlSYu7iRWmE7L+6fzLvTyXdOx3Z9hPV9LSgy1ngPVnSe5udahaY8cOYIV+cdwqMj6605BQUGN2zwRb913ZmZmjeslJFhf39lz57BiRRrc6X2TX2Ct20OHYhGQdUTe35turTe94cL+S0qs64nPbWOi/S654GeJ/eukvudCXl6evI09HIsV8TWXMy83D/v27sMZwxnbvIyMDOxesQL5pgt/L9dvWI8gVVC12zHr9fJ25apVuLy4GAe2b0fOkYoDD5zLPAGzxdxodReTfBwlOdZ9FBYW2rZrMl0Y/vnAgQPQpR2C7/Fj8EhJQeyKFchJSEXHi7a1a9dOFJ6qXLfq9HTb34w1a9bA7G0dLKkquUnp6FLu87m+50mLjAwkxsai0O/SRoF02TayZ86cQUREBNRqa7FE04Po6GgkJibKQLYqixcvxssvv4wJEybg/vvvx6uvvooWLVpUWu/RRx+VU5nIyEiMHTsWzUWcKHXZn35fMvKOp1Sar9NqxeC01vs668hf/gH+tnpSKi+c3EOHDgHCusHR1bVO3MVPf/+EU6mnZFvv8rTy2NedGK5Y1Gve1gtBaY/u3YEMLQyJ54CMqp83ZPAQdI2o+4fTguQdQFY62rdvh7GjrR+5+87kALs3y/s1HVvl4VTg4C54eGgxduzoBp0nc09sAvJz0aVLF4wdEoPCE4VYtGURfHx8atz3iTUngNPHERwcjLFjqx+idqshFkiOR0TLlhg7tg/c6X3z7uENQGE+unfvhrH9o+U8y6FzwKE98NR6YuxY6xC1/9mxCigtQd++fXFlF/sNUcvPEvvXSX3PhXl/zENyVjK6de2GsZ1qLudXy75C7569UZhciG3Ht8l5ISEh8vVlFGfg5YUvy3lXDL8CET7l0wAVrVm2SN5eNWYMcFSHgQMHAtGDKqyzZWcclAfXNF7dbTqCM0eygEzA29sbY8dah6hVbfobMJ8forZnT4wVI3slJaO4pAT9xo5F3O7YSiNw9evXH8M6hFTaRUlCAk6evz9q1CioAgKqLU7ZyF5lr6++50niol/g260bAh3ob7dDBbJlQVl9hocUWVsxuYtktQoW5MpvaoUmA/Z7eaIlR6d1a0aFCT/5+sCz2AyUAEqfA1ibqIPSU40stTeuKyiEqhH2syphFZRQ4srW1iCmIXINufjz9J8wF3do8Da2JG9Bpj4TFlT/YX2pzuSdwUn9CihUreRjk9mEpSeXItI3Ev3D+zfZfl3d4czDOJh+ENe1vw46dc1DcZN9bUzaiLySPIxvM77S3+XqKD2TsTntHIZ2uAMGk0G+1we3HIzWfq2x7NQyBHkGYWiroRWek1KYgh+P/oiRUSMR7h1eaZt7UvcgS2/9taq5rU1cixJzCcbGOE7QRg4eyEZFRckmAkajUWZlRRArsrQiK0tWcwMDMO18ILsl7yTWtffFg7Dgzs63Avia1eTEbuhwA4pzitHKJxIW7Krz8077n8NvIUHoUGyEwpAFj4gfMGvtD1CE+8GiUCDMaMKQSyyb+GPz6DrrLxrrJq9DsC64QdtZcGQB5u6fi67+IgsysUHbePCfB2GymBChfApAxex1Y3l/z/vYV7wSmqArAHTEztSdeGHLC/D18MWWf21pkn26g5e3viyDWfEL0qSOk+xdHKpGqalUvs+E9gHt0SmoU53qyrPlYiyKP4tuLQORUpSCT/d/ihFRI3B/r/vxn03/gQIK7L9rf4XA+IuDX8jbY1nH8NKQlypt84kNT6Ctf1v0atELm5Otv/aU56/1xy2dbpGfCSG6ytnKS/nSPWvtLHm/W3A3+SWWHJNDBbKhoaHo06eP7E7r7rvvls0GRJOC6poVuKtWEBe0WdtNRhiNCLao8GCPaTjGQNapXdfuOmjjtAj03I/65B/KfrOwyL8NF37BEEHshfmXpvwvI5Zy+6gvM6ztLC3nbxu0DUvZcxu+jdqUvUaFwlJhn7X9QkS11Ov5+mM9Oo/6vd/Pv1/Ev3LvGdtxr2Fb1S0Tz53ZZ2a1QapGqcFzg55DY6vwmcf3vUOzS+v8hx56SLZRFdnX0aNHo3379rZln332mZxE91tvvvmm7FqLiIiIiMghMrJz5syRU1U6deqErVu3NnuZiIiIiMi5OFTTguawaNEiOYkurIiIiIjIedmv4z87mTRpEhYuXGjrwsqRecCIFR5PWh8UpMFc1iloJQpAUe5QqkUvs+Qy6njFsA4GPKxeXKd1Q5CLXz1ewMeaD6pfKXk3MHcIsPa/aGwqox5LPZ7Ff0yfwikUnu+zrLQZvgCveBb4ZBiQZu2/tUkcXwnMGQT8/Qzw6eXA8serXzdutVw3Ou77pisPUZa1L+oaZZ2y3qo8gMRtwB8PA/PGAj/eJjrQrvo5KYeAT4bKc3jAoReszxV/M8XfySUzrO8B8V4Q74nUWGDH53X+zK0TjQ7hqRvxgGopj3ETcbtA1pkoFRa8Ybzd+mDksyhWVtHJcUBrwCcUUJ/va/SO34Dgds1bUGpaCjUmG56vdbVkSwiGKGPrtMloRSr6KuMwTrmz+pWSdgNpscAJ6wAljUllLERP5WkMt9Swf0eSe36AEbO1L+cmdfxvIPUgcG5/0+0jYTOQfgTYNhdIOQAc+6uGdbfIdYNT2VsDNaH8c3VY53wf610nApf/H7D7a+DMNuDYcsByYYCBCs7utSZ6rnoNx2LuBqatA9QewO3WPmXle0C8F8R74uw+a4DrZ+12r1H0m4LTMZNxhaoJ389uzu2aFjg6VU42hu2y/lERF3323xELjfjbGdoZuDCoyQVaL1hMOUgrG7UsonfzFpiaxSFLW9lXRU2KLRUHTpiww4xWmRYoujZdR8PdMk6hxzfLUBR0H7z69m2y/YgRedL+9zZUQYGA94URy+oqd/lyFG7YiJCZM+AOzIWFSH37bWhaRiBk2n1Nvj/tjs1Inr8NIdOmQVvu4l2iJiEC0RY9676+VzDQYTQyTpnkYEEZn32O0pRzCAvxrZzN8/QFVBpYTCb8a50JahNQUroEZ0+dQ+hjj0IdXM+uB1Ua5PmJDjMb/9ofY0YG0t59r8IACOdeehkKD43tccj990Pbtq3tc6Fk3mdwNQxkHYxX7F54Zp6F52vPw+f3J5FYFIbveulwX5toYLd1nWP9Q+DbvhsW6jZBhK3GtDRkf/8DIt6aDfVFo0KR8/IZMQJe/fsjO7ItsC673s+/abMZPnogP7Tp3uZjEnchInEHcn8PbdJA1nDyJLJ/+EHeVz6lhqmegWzWt99Cv/8APLt1AyIrjuTjivRHjyLnx58AjaZZAlnvv5cgb+8OeES3RosZDGTJsaV/9BFgNML/1mB4VbOOJb8AN2y1dsFVsmOOGGsGPsOGwm/8eDiKwu3bkfvrr/K+wsMDLf/7BkqTkm3L8/7+G/krV0F7/3Tb54Jp8wbMHngXXKmhEANZB5TnG4So3j3ge1CPhYYrEdZjC6aVa7NT7KvBuZFDkLRrM3qfH8NOoVLB/7rr7FdoanQKT08ET52CvIxCYF3j/7xPRESuw3/ChAqPDSfjKq2j8PbBtogecCVsI0tERERETsntMrLsfouIiIjINbhdRtaZut8iIiIiouq5XSBLRERERK6BgSwREREROSUGskRERETklBjIEhEREZFTYiBLRERERE6JgSwREREROSX2I0tERERETsntMrLsR5aIiIjINbhdIEtEREREroGBLBERERE5JQayREREROSUGMgSERERkVNiIEtERERETomBLBERERE5JQayREREROSUGMgSERERkVPiyF7NpMRkwZt/HUV0kBduGxhdYdmexGws2ZuMkZ1DseN0Fno3V6HIbRTojSgtLkVCZlG168xdF4cWvloM79AC646lYUD6WUwAcDa3GCtiz9nW+2NfMs5lp6PQYLTNKy414aWlsUjPN9jmfb35NE5nFEKrUWHmqPbw9dTYlmUVllTY9zdb4uVzu7T0w874LDw4oh30pWbM23QKQcVmDGlpsa1bdq/UdGHemawi+R4qe61lvtsaj3O5etx8ftXUPD1WH0mT90+mF+DFJYfQLcIfk/tH2Z6z5mgqDp/NrbKOLLDI5wR5a/HQyHZQq6rPBVgsFnyx8RSKSkyYMbJ9hXXP5hTj8w2n5Dr9YoJwba8IXKq4tAL5eoXRpiyEnC/xlxtPIU9vlGXwUFdd3qISI+b9cwIZBdbjl5qvty3LLipB4Pnb6uQUlSIcwNqjaeianIvurfzlfJPZgk/Xn5T3Q321SMm7sN2a/LonCUfO5WFgm2BsPJGOu4e2QZsQ7yrX/W1vEv6MM0EVlSrX/feQGLRt4YO6yiwwYM7ak+gXE4jxPVpWWi7Ox2X7z8r7IzqFYuOJDFzWOhATelZety7+2H8W+87kyPdEgJdHhWVGk1mWReehxH2Xt4VCoahxW+L8mbfptDy+YnuaGs7Hmoj3xSfrTsrt9YkWry202nXT8vSYu+4khrYPwZiuYWgKZrMFn6w/KfeVry+VnyUvjetiW743MRuHS/PQtdxzRNl/35uE2OQ8zLyyA/x1Fz5vxHn41p9HcOykCUnepzGkin2KOvxnbxJM+XqkK9X4YPVx3FpLOcX75oN/TkBfYpKPxedCdLAOh0QZRrVHap4BC7YnoEdmESLF8TWb8f7q4/D2UMvPEmocancc2UtMkZHitGo+p/Ms+HTfSeg0qkqB7Neb47F0/1l8szUBkwpLEBGgQyt/HcwqJV65vhvmnraupz3/RyjMz7NZy06OLdzfE/sBBPtogYyq1zmckgdTWj5KTWZMH94WOzcer7D8xWu7yiBj+YFzmL85Hv1aB6KNwRrUpOUb8Mm6U5CRCoAP18QhO996Dg47//ykrCKsOZqGq3uE4/lruuLVZYfx0h+HccegaCzenYT+MUEV/ujpjaaK+18aW+FxuxbeyCwske+JbkEKXBlWCNVFrym+XFD+295knEwvBHyAjIILAZcog/gjNr7EKJ+/KyEblhjgx/sGyaBHBM+vLT9cIZD9eE0c4k1F0PhVXZeeHip88M9xXNc7otrgSsg3GPHGn0flfRGotisXXC07cBYbTqSja0s/vL3yWKMEsot2nZGvr6W/Dr+eSMY0MdMCvLb8iFx+dfdw+UWhKtnFpXhn1XFMv6KtfPzvwTHYctJ6Mh1Nycdg+WWhuNp9n8kukqdHWr4ee7cn4r839pDzxeP/rThmW8+rjQGqOnx8iS/94rz7YqP1w098CXp8bKcq15391zGk5Fmw6ttd8rG3Vo0nx3VGXf1zNA1fbT4tj0dVgexXm07LQN1kscjzUVh3PK3Bgew7K4/Jc7dHK39M7NOqwrLErCK8t9r63rylf3SFYKwq4ste2fGd0KMlOoX7NqhMfx08J7+89o4KwOy/j9YYyP4dm4Kvt8TLBExTBbLiC5U4b6YNtwbz645nYGuHzApf2v7KSqkQyBrNlvPngl5+0bi63LHM1Zdi4a4z6BUI+dn0VxX7PJGWj7h0Fdp5A+L7Q21fIoQ9CTn4accZ3DrA+vnx4ZoT8vNG6BnpL7+wiHNmojIDt6qt76H3V5+wPf/pqzvLL96julRf34KIA8SXf/Fliypj0wIH0ypABz+dBkqlAmqlAncNjrEtE4/LB7RE5c8HL4+LQ73KRFasV1RApfkikHrm6i62wGx4xxboEl5NJFeNDqE+chvje5yPeAHMHNWhyi9enpdwDlf35yUySFen5w9uG4zB7YJlsDP18jb13r94jTVlYutKBLEXf6m9VOILyMQ+DQuKVUqFfG1i+r8xHeGpqf18ulhUkBdc0eiuYRjRqQVcWccwX9w5uDUcyVPjOsNHq0aIT8XMdUOIX5uuian5fRsZqEO4nydCfLT495C61UWwj4ftfVP2N7quRncJk1/Q/Mr9WlWV63u3wiOjG/aedAeMiIiIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickoMZImIiIjIKTGQJSIiIiKnxECWiIiIiJwSA1kiIiIickpuN7LXokWL5FRcXP0oNURERETk+NwuIyuGp124cCF0urqNAkREREREjsntMrKOZFXCKhxIP4BSy8AK8y0WMz469gOKAnyAHbNhMFnHvCe6WLJPNjzqMCZ4vCkdW9Rx6GX2ws6kz1Dgk4OPvf3RwmgGzp9eaxLXIFn5B7ShxUjWj8FPxt3w0HrAWGxBqc8627bM3rvh4ZmPkoyRUKgK5bxic36V+/3nzF/I9lyH3xMDsKfAS57vQrYyB6u9dNjnoUXU0Z8BiKFxK76OfGMatKF/oFjZASuSUjG+7P0BS4V10w3x2J2zAnlmfa31cK50B3bnZyE280608WuDBcc/hSUoHj8cycBtnW+T46sXqmKh0pyrdVubzv2Dhaes49wLV0RdgQ1JGzAgfIB8nS08I6AJ3A2FsgQm81BsObsFG5M2Wl9DySAAvigxGVDkvQyzd+xAhE8E7uhyB/5Rm7E/MAD3GouRlXsaC48txLg249CrRS/Uhd5UiBzVetSfBT8e/RE5hhzc2+NeNKWV8SsxOGIwonytY9RfzKgT59gZlGSOAsxaOS9Ln4WvDn6Fy8Iuw8jokXKeqJsS3x1A/uWARQ2lNgU78zdi9o5AjIwaiQEtB9i2uej4IpzKOQWNSoP7etwHXw/fi169CZ/u/1TOLzsXKlEWQRuyDgZNBwAjKizakrwFm85uwr+7/hth3mHINeTiy4NfoltINyTmJcJL7YXbu9xuW/9M0VHM3vE9JnWchLYBbetUb2lFafg69mv0D+uPQ5mHkGcohCYoB6VZw2zrxGbEYtmpZfAyeGH/zv2wWCzoFdoL42LGyeVLTy7F6dzTsg68NF5IyEvA2vQvUWRph4UnV6JUDpc6vMJ+d6bslJ8Pd3S9A4XGbGhDlyFT64HZOzbb1jH5pQMKU62vYX7sfNzd7e5K8w8Y8zB7x2wMbDkQnf0GQBO0HvMPpcBsMaO5ZRRn4NujX2Fys++ZGoKBrB19sOcD+SHSRSU+qC+M+V5YWogFp5fjRvFAocSDvR5EjH8MpsEfnXPjcE10O6jaXC0/1IZnhACYbc+XQc3BooE+5Tp4KJfhyf6PYa9PBoK3rMRXHjnYoUyouKpFiWcHPQ2t5WVbwLe65BC2qpPwj3jHpx0HIoHP4A+VxQJkWJ/30d6PUAJfqHyS8WeG9Q+Uyd8Pw82A0Wsrnh34LF7f/jrMgX9AnLGm4hgotWlyvQxDYpXF/uTg+zAr20GBICgVSvQO7Y0unpOxLf4L/F9YgXWl7a8BeKNSIHuiaB08gjcjHZux/qzFFsjO6vMwPtjztW29fbl/4lDBX3WqxmJzNjKMcfjh8A+Y0HYCViUtARTd8OaON3FTh5vgqfZElmY5zEWhMOkjatzWN0fmoFdod4R7h8s/9N8f+V7O/+7wd7Z1PMOttylFUzDv6GcycMgszoTWmAd/3ITT+Ydh0G2EyTIRb+18CxPbT8QHWiPivfzQMS8Ox45nyu2K9/o7I96p02s8nrcL+ZqdqD8T3tgujgNkENhURoRPQpJ+L5bELcGMPjOqXKc0YCm0qjyYi6NhLOgm54lA6pvD32Drua22QPa1ba/B4meBMrMdzPpoaAK3Ib0kHSdzQnEy52SFQPb1ba/j+vbXY0P8BnQP7o6rYq6qsE+jKgVz9s2R98vOhYupfY/AI3gDCo1HATxSYdmnBz7F3rS9CPMKw7+7/Vt+qRFBZ3ni+JbZkv4bDuVav3A8NeCpOtXdX6f/kueXmHw0Phjf5lp4hi1Hac5ltnXE+SICWSHaGI2uwV3x3q73bIGsCBbzSvLkF4JhrYbhtxO/YU/uUrns6BlA4R9Q6bWJgFx8EfPT+uFYgRlq38MA+sj3dBmT3z9QKY3Vlj3IM0gGsO/ufhcT2kyosOza/EIEhkTiTEGyPAavDGwPz7C/8MFe6/u65fn1btVGIrdYhd+yhgKhtX9xbYg+8MStna9HUXpmk2yfGp/bNS1wFgEevngipwBP9H8C9/W8D2qlGgOhwxNZOQhXe6OFVws83v9xdG/R3d5FpWahQGn2EGhUHhgSMRiPXvYoupV6I7zIv8p1/9X5X9CKxGU9BVuGw1wcWXmLFh1u6nhTg0ruaxiF66Pvl+eymGK8e6NjqchoNcxdXe+sFPTWlZ8qGq08+tgeB3gEAdnWP/Dlleb2gcVYMWNXlVs73ypfkwgK6kIEz/3C+1WYpzB74+G+D8NdtPXpUefsckO09OgpM9hVEVnICO+av6A4C3+tP2b0mlXjOh0DO8rPgsZmMoQh2HCz7T0tJlgqB/7liaBXBPhViTAa8YSuHW7sINM31eqo9sEt5o4wG5ruGOqgxMw+M3FvtwebbB/UuBjIEhEREZFTYiBLRERERE6JgSwREREROSVe7GUv88SFBsmAuh7fJbxDKt6SWytQBaA/duMwrG05LUoVWiAHGngCH4p2oBeu7jcprVd+1yg3GY+kv4iV3p5YBi/b7BaKXIRYSoHPR4gGZDYRyIA/rL0WaEtzEVlyCvjvjQg3GvCXR6icf38R8EDhXIxa/Big+wVoc3mVu17v8X9IRyA6Ks5gcsmLALohuOhkrUV+TL0Q2UmbsN/3QnnLW6L+D1oqMpBqsraLDSxJxrCk/2/vTqCiuu89gH9nYxMFBURFwIAbLiguuG8xabBVkzYxm0Zf0zRpbaJ5ntIm5/hi7DO29kUTxZra9DVR+1IVY2o1sSfueyIuqAgqosi+IzAwwzJz3/n/RwaQAYyK5s58P+fMYWbu3O03v7nzmz//+78HkKGJAsKannBSL9CSi3cMf8IHmlHIVm6drdWagyuAC+uBTmLkBceC4qcDgf5Aynxg0Ax0NFViVeoT2GdtesJRE5d3A7U3bcvN/AZYHgQ8Mgka39nA2pFAdQXw2hHgyErg1N8Qa1VwpdNopFsScQa3+k5bHZx8c2w1cHwNqvWd5ElnQidUYZ/hPzGj0fsu9Lu2AZHZG3FTcbxv6yxLEeGehj2ahj6vYcbTwLLHgeBodM2/iN1uXphW84fmMxdeAv7blicY+GPgJ+txRwpSgI+nAlVFgI/SpLu0yJ/onL2AdxGgE0Nf5APrJwCmm0BwV2DrPMCaA/SYDPS6g3XtXIjVaZ9hf8cPcc2n6egy983NG8CyQCBqDhAtTtBsZOtcIHUvEDYZuLoXEHne2XHf7e57fglk7AeGO87ru/a3GCDi1qgIB/+IkRYrrroHw7+uAPhjOOAdKI81vh7luNlS25ixADAWAh+Nsw1QcjuvLkDi/wHlV4HqDHTa9XPb+3fLc1X/wGPbfw34BAEdJjWZtUbRoQYGdFjRE19ZPHDREIKxX1wB/qkB+j4BXP4KNcHTAEdDx3v4AuKcMb8+iMrchKEimQY1nJB3P4Vq8nHW/VWkKT3wTM277bIOV8UW2YclLwlQ2h6qpIkn1wFvnAGixMku5Op2dn0NWywNQwBZ9Z54rua/UCe+AUqu2epYALu8n4bJQ3zZtMFSjX3eM5CrdGnytK/GCA2sQN7FJs/7a8pggK1Q0lprEVCbC3QKQvFPtiBCmylvgBX9LanQWcxAcWqLqw7WFmKE9go6aUwI0eTL5zxFEdeG/ppMaFr5HPXX3ICfpgIaa6187GGphFa83lzW4jzuSjUMGou9SG9TQXKbn2VtbTlgvglYaoGqErjXlcFNqYGHUcSoBcZcoHuk7X55LlBjBPKToFXqgKIrQEWurZgruAhMeRtJ/k9gQNlh23Q7xXEhOOoXOBf2c/tTHTUmBGluDV/RiE9FKs50m4U1dY5PwumDG+ikqYIHauzPBYgRLOpMwPVD0FUVIULreEQLVBYB/aYB0/5g24c7ZbUA2aeA0vRm++cLI7Sw2OIl11EA1JqB+Sdsj3PP2uY32nKsTfnJ8n3yrbyGdlNVCtSZ5bqar/8iUFsJXP4SGPUqEDq2xcW4F6fYXlvjeCi8uybyrvZWFRgUhVz/0fCGCX7Wkob8K0iG1lGu1asqBsQwWuKz4siInwGT3wZyEuX7oy+50mRyN0uu7RhS0vzHbQW8MLn6A9S9dlz+cJ+gS8LlwbFAxAwg5V9A3xhcjraNxtHMlLeBBWeB+d/gs5GfY0X4RmDmWrSH7poSdNYYEaFpOsoM3Tu2yKqJmxfgF97kKY3B0OQvqV9r76lGb0Du4v9CUNwaWDQG9MqqxpxdddgTpUXWeMhWzY5WIPuEL6x1Gli0QJVWtODYvtx65Sl4+WsLtAqQEqLB5sm237LG91fgZ8drURaYjUkZJvxYZ8EXY7Wi2VUy1AK5Rzpj2eGGIsmj5jT8KmtRowd6pZoBvxRgiDssPiEQo3rlfuuL3xxWoLF0Qm10KUwJV1DyP8/Dd8IM+5HHUKdgwb+syK7sjKAxpdA2aoUJyKrCsgN1OBipRVr35qMUPJ1yGgFZRfB5TCOaFFtUkNgRyMqFVdt0GZqim/jl/2YjXfdPeJ+xIP+bNxG8chXGnbKgW4o3fqHNRZVSiqwJA4DeGnhWK1i4w4rspF9D69EwPFVZYhHGH3PH+clWnA+zxdPj1mvNbkDczDtvLyhdtgK/OlqHKi1gGKMF9A1N4HVmLXJ21sE3eLu9NbHwb5tRuTcL3RfpYNa1EoTbeXWBtbSh+HRk2Nn9qL6YjLrJQ+B9sxIBiUVYZY3D9R5ixImp8jUVZ9xQcdMPHc1VUDRaBB/fgxLDAIiGXUu1BtknOkPvYQWGNl++73Uj0v+dDp/poejshvaj1crj5qtfWVFV6AvPJwGvY2lIX/s8At5cCBh6ooupDG/u2ITMQAu8TbDnwu3vXFBZORbuq0NycDnwMzxYXn5ArW24u/bSsUrBgh1lyL+6BOgH/MceC5QsH3gH2X4Iinys1dneLHezCRkHu0DvaUWPUW3/6BTEceFXOy2oOBgLjxjbj7+Rl60Yse1zFEb2RucjHTFRpFdE28vyMBpxY78fFB8Nigb6yPe4LNEdFcV+qO7jjoosM4r3+KOL1gxrlIMRFXQ65K1YiZ6rV8Otix5lXiEoMhpt37ONWDWQx0vF3R2Fa+JQnZ6OykOH0fmll4B+bbfS9y3NwJMXDqOkVwf4PFKFghMdscoUh3I3L1jiTiP9SjKg0cB70kSUxsfDa4RtVBONXg83Sx3eSvg7lNopgGfT7wNT0kUUxa215bfV8Vi74jukdPNm+SPCeOgwdH5+cEZskVU5j8hIBP9lPUI2bXzYm0L3id+rP0fQhx8g8Dexzaf9+S+ozclBdaqtdTMqqwR9c4DhVxtaQ/SiQeqGF3qOK0WRf9ODX0SmAr0VONNbi3HJDfOYd3+F8AwFwxKuosBfh5veGkSlNUwXX+6V2R7YFa21344OCMa/J3jhl7/S4Ua4O/yviRayBmXpXjghRocz6mC+qUflueswnTuHTudPNVnuqMsKjFmesFQ3PRx1T6+S+zbyiuOWnnGZV+FeXAv/nNYPY2UZnsBTMbgxOKDJ8/rrWehz3YTHryZgzCUFVQcOoa6oCANTrajuWoejkZ1Q0NEToVfO2mJfDgxNA8q//BIG0bJ6S8Xlm+iSr8OQ6w3b6VcBDE9TMC5FgVvDS9tUtWs3zvfToqMJ8CjQNW0YK9ejMluB16kz9ufK9x+DKcsMU2o27re+V89Aya2EIaMIAUWl0FiBk90iMPp6w/tnuqFHVYE7AsrLkPv0XJT16oMO6Tm27TXqUZnnIfPAkc5XjTBllKHi2yQ8CBMvKLDkusOvBPA8ly1zsfK4aMUDepXnoX9OOh4/qzTJhdv1LSpGRBYw+nL7jGH6sHUvAYak16Jih21c2fEXFSDPDf65zfe3U1mp7f297vj9bcmkJAW1JxLQpdh2JZaoawp8UvNR9PkxuBXoMfDKnQ2t17GwSOaeOaOhPa7mhg5Vhe7QZOXBeKkIpmI3GC85Hg+255/WwlpZCfPF1v8bsORFA94Z/ywq//x3aH06oWhNnMwd46E7u+hIZFEaupWUoCLTA3UmHcx5BmzvPRHDCq/AGv8PdBg3VhayhavXwL1PHwTG2o77mh5BWDLmZUzKToRibt4voupUgtye7u+91+K6uy5aBI8BA+SyTYmJMO7bB2fEQlblxNVnvCdOhOdA26DhpH46b290iomBPqBp4SUY+vaD1tv7jpbjGeD4inDFHTVICm35yyKnqw7ZDn+4K/gmQmu/JYV2xaXebqjw0qCom+P/CJwX/0Bwu4sBbe+3vmHfaejZ6q4WJIV3wI0uLfd7bS+XwzQoe/CrbZPFU4vEgJbH/63z9kFFN8dX6iL6vnHv3Rs6X3Hxh9ZdC9TgVPdwKN16wBB4B33m2yCuIXE0aAgsty4m4RUdDUN32yUf3Hv1anLcT/Jr/Ypvhm7d4dYzqMXpej8/uIc1XGzJWbGQJSIiIiJVcrk+svHx8fJmMjk6hZGIiIiI1MLlWmRnzZqFrVu3wtOz0ThCRERERKQ6LlfIEhEREZFzYCFLRERERKrEQpaIiIiIVImFLBERERGpEgtZIiIiIlIlFrJEREREpEosZImIiIhIlVjIEhEREZEqsZAlIiIiIlViIUtEREREqsRCloiIiIhUiYUsEREREakSC1kiIiIiUiUWskRERESkSixkiYiIiEiV9HAx8fHx8mYymR72phARERHRPXC5FtlZs2Zh69at8PT0fNibQkRERET3wOUKWSIiIiJyDixkiYiIiEiVWMgSERERkSqxkCUiIiIiVWIhS0RERESqxEKWiIiIiFRJoyiKAhfk7u6OgICAB7Y+o9EIb2/vB7Y+NWBMGBPmCT83PJbw+MrvnIfHqILapLCwENXV1S1Od9lC9kHr2bMnsrKyHvZmfK8wJowJ84SfGx5LeHzld87D09MJahN2LSAiIiIiVWIhS0RERESqxEL2AVm0aNGDWpVqMCaMCfOEnxseS3h85XfOw7PICWoT9pElIiIiIlViiywRERERqRILWSIiIiJSJRay7Sw1NRVjx45F3759ER0djeTkZLiCXr16oX///hg6dKi8bdmyRT5fUFCAmJgY9OnTB4MGDcLRo0ft81RVVeGFF15A7969Zby2b98ONVuwYIGMg0ajQVJSkv35u42B1WrFG2+8gfDwcDl93bp1cJaYTJ48GWFhYfZ8+eCDD1wiJmazGU899ZTcL7HfIi/S09NdOk9ai4mr5onwgx/8AJGRkXK/J0yYgMTERJfOk5bi4co5Um/p0qVNjrFOnyNiHFlqP1OmTFE++eQTeT8+Pl4ZPXq0S4Q7NDRUuXDhQrPnf/rTnypLliyR90+ePKmEhIQotbW18vHSpUuVefPmyfvXrl1TAgMDlZKSEkWtDh06pGRmZjaLxd3GYMOGDcqjjz6q1NXVKcXFxXK5KSkpijPEZNKkScrOnTsdzuPMMTGZTMqXX36pWK1W+TguLk55/PHHXTpPWouJq+aJUFpaar//xRdfKFFRUS6dJy3Fw5VzRDh9+rQSExMj86D+GOvsOcJCth3l5+crPj4+9oQRB2aRJNevX1dctZDt0KGDUlBQYH88cuRI5cCBA/L+gAED5Ies3qxZs+w/ApwpFncbgx/+8IfK1q1b7dNiY2PtBye1+S6FrKvEREhISFDCw8PlfeZJ85gwT2w+/fRTZfjw4cwTB/Fw5Rwxm82ysUwUpI2Psc5+LGHXgnaUmZmJHj16QK/Xy8eiqT8kJAQZGRlwBbNnz8bgwYPxyiuvyEvMFRcXy39VNL40sPg3c308xN/Q0FCH05zFvcTA2eMTGxsr8+W5557DtWvX7M+7UkzWrFmDGTNmME8cxKSeK+fJ3LlzERwcjMWLF2PDhg0unye3x8PVc+Sdd97BnDlz8Mgjj7jUdw4L2XYmitfGXOWKwIcPH8a5c+dw5swZ+Pn5Yd68eXcUj8bTnTVW9xIDZ43Ppk2bkJKSgvPnz8v+btOnT3e5mCxfvlz2qX/vvffkY+ZJ85i4ep5s3LhRNpAsW7ZMFmuunieO4uGqOXLixAkkJCRg/vz5zaY5e46wkG1H4peiuIZxXV2dPQnEh060yjq7+n00GAx48803ceTIEVnQCqJ1tt6NGzfsrxV/60/quH2as7iXGDhzfMRnpf6g+frrr8tWFNGS4Coxef/99+VJFrt374aXlxfzxEFMBFfPk3qiYeDAgQP2x65+PKmPh8gFV82RQ4cO4dKlS7I1VrScitrjiSeewMmTJ50/Rx523wZnJ/rrND7Za9SoUYqzMxqNTTrir1y5UpkwYYK8LzqVN+50HhwcbO9DLJ5v3Om8a9eusoO52t3eH/RuYyDyaOrUqfaO96LDfnJysqL2mIh9z8vLs0/btm2b3Ld6zh4T8fkYNmxYsxMbXTlPHMXElfOkrKxMyc7Otj/evn27EhQUJM+7cMU8aSkeNTU1LpsjrR1jnT1HWMi2s0uXLsnO13369JGd0ZOSkhRnl5aWpgwdOlQZPHiwMmjQIGXmzJn2E9zEQUacgdy7d2/ZyfzgwYNNCuBnn31Wntgh4iUKfzWbP3++PLjqdDp5kl/9CSt3GwNxMBHLDAsLkzdxNrczxETss/hsiFyJjIyUZ8kmJia6REzECA6iPUFs+5AhQ+QtOjrapfOkpZi4cp5kZGTIE3Tq910UF2fPnnXZPGkpHq6cI60Vss6eI7xELRERERGpEvvIEhEREZEqsZAlIiIiIlViIUtEREREqsRCloiIiIhUiYUsEREREakSC1kiIiIiUiUWskREbRBXyunfvz+GDh1qvyUnJ6smbp9++imeeeYZfB98n7aFiNRP/7A3gIhIDbZt24ZBgwa1y7LFZaz1eh6OiYi+K7bIEhHdA3FN9xUrVmDUqFHyOueffPKJfVpqaip+9KMfYeTIkRgyZAjWrVvXZL6VK1di8uTJePvtt5GdnY2pU6di4MCBmD59urytXbsWOTk5CAwMRFVVlX3eF154AR999FGzbampqUFsbCwGDx4s1xcTE2OfVlFRIecT00aMGCGvQS/k5eVhypQpGD58uFz3ggULxBUf5bR3330XL774ImbMmIEBAwbg0UcfRUlJib1lVVzL3dEyhU2bNsmYDBs2DJMmTUJSUhLzjIjuOzYBEBHdAfHvcA8PD/vjkydPws3NTd4Xz3/77bdISUlBdHQ0XnrpJVmoiiJQFHSiW4IoREePHi1vorgTqqurcfDgQXn/6aeflgXl4sWLkZGRIVt/RSHao0cPPPbYY/jss8/wyiuvyMJz7969+Pjjj5tt4+9//3ukpaXh1KlTcHd3R2FhoX2a2L5z584hNDQUb731liy+169fD19fX+zcuRPe3t6wWCx48skn8fnnn9v//S/mS0hIQJcuXfD888/LeUTh3doyjx07hs2bN+Pw4cNyO44cOYLZs2fL1xIR3U8sZImI7rFrgSjShIiICNlFQBSb5eXluHjxoiz+GreKir619YXsyy+/bJ924MABrFmzRt4PCQmRrbP1Fi5ciNdee00WsqJQFAWyKDxvt2vXLtnKK4pHISAgwD5t/PjxsuAUxowZg7i4OHnfarXit7/9LY4ePSpbYgsKCmQf4PpCdtq0abKIrZ/vwoULbS5zx44dsmgVLbL1RFEtWoyJiO4nFrJERPeocUutTqeTfV5FUejv74/ExMQW57u9GBWtuI6IVl6xjkOHDsmW2P3799+XbRRWrVqF4uJi2boqXrNo0SKYzeY252ttmth3UaT/7ne/+87bSUT0XbCPLBFRO+jXrx+8vLywceNG+3NXr1619zG9negrK/qdCpmZmc2KVdEqO2fOHNmPtW/fvg6XMXPmTHz44Yeyy4LQuGtBS0pLS9GtWzdZlObn5yM+Ph73SvSpFfst9qO+1Vd0dyAiut9YyBIR3QHxr/bGw2+Jfp+tEV0MRN/TrVu3IjIyUhagomuAyWRy+PrVq1djz5498iQt0So6btw4+Pj4NFm/0WjE66+/3uI6RReB8PBwREVFyW2cN29em/slTu46fvy4fL1oRRX9ce/VxIkTsXz5ctnfVuyP6JKxZcuWe14uEdHtNEr96alERPTQiALXYDDIAjg3N1eOdLBv3z7Zslt/cplokb106RK0WrZBEBEJ7CNLRPQ9IIbqmjt3ruxfWltbiyVLltiLWNGS+/XXX+Ovf/0ri1giokbYIktEREREqsT/TxERERGRKrGQJSIiIiJVYiFLRERERKrEQpaIiIiIVImFLBERERGpEgtZIiIiIoIa/T+CdgMlyKSqRAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "times_ch1_4 = [170.448, 230.292, 210.383, 238.595]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.14', 'Ch.10', 'Ch.6', 'Ch.2']\n", - "for idx,s in enumerate(single_ch_spectra):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Current comparator background spectra, SiPM-Channels 1-4')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "aee0452b-0a8b-4f2f-83e7-94bed99372dd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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CWQKTzTMZnoJMAGlOlnm7dDFZOrNNebNrJ8K0VcxbN6b9bf369a3BtC81mSYTlJqsosnImKDmeEw5TWbq6quvturPZGdMu7+8/7TNP6e8dWK+pJh/tnm30bS3NP+8T2QbTWYrL7NdJsBxlc90q2SuUjdXOJssdnx8fL59YaYvqcsek+k2gZ0J6t5+++3c90121hyHJuvoUlQWsyCzjWb/582+mjai5lh2BX0Fj2uTxSoLs/1F7e/jMes3mVkTFJljwWTli6tr89zsR7M/TXbUBHamfWvBz45rm0z9F9cNmyvgON5npzT79ESY9bdp06bEXgtcAajJrhcMYsvjwQcftII6cwyaNtvminyTIc6b2TV9qpq2oaZ+TX0WPDeZL6FmftPGvSATjJa0XQXr9oYbbtA555xjZahd59mKVN7zrGnTbs6TxbWbr4wyu5gvTKZdcF7mtenmDJ6NQBZu8eabb1r/iE03K+ZCEfPTk2lUby60cvWtaC76MNkyc7GDCYZMtuJEu+gxP6mZoMRcMGF+YjTrnjJlSqELGUrSsmVLaz5TFvNTcVHf/k2AbrJ85h+xuZDJrNMEDq6gw/Qr6bpwxlx4ZS5SMMt1/dR4okzmylzwZrbt77//ti7cMdtsmGDEBD7mH6jJwpptLk3gY+YzP4WaTJUpqwloC2aBzQVv5sI9k8kw2Rjzk/d1111nXShk5jNBtNlGcwGI+edZVuYna3NxmumezXTBY4JCV3bLlM8EoWYdZjDlM/XgYrrRMZk589OuCULNMkx2zOy7vMz+Mhe0mYys6arJFeCarKU5XszFd6ZOXf9cS8qGmn/EJttlttmU3VxcZfZF3gunzHFtLowz481FcWU5Bg1Tj2YZrmPZBEIlXTBnmOYT5gIsE2SbwNl85lxfCFzMxWzmM2gGc5ya+jMZWPMz9O23324dXyZzbb4Emf1gmgC4un4yF5uZi9HMesw6TCBsLoIzzBcgExyadZoMV0lfLI63T4/HZPLMPjABowmoTFlMmcznzVwEVdVMd1nmM2GOO1fzAVOvJkh3DebLovmyYH4BKYo5nsxnwOwTExSbz645X5p+Zc0xaraxNEzdmuYlpl7MxV55f4GoKOU9z5q6euutt6zsuTnmzPnHdIlmjh1zUWDBQLM45gJT86Uy75cz8yXcfHEwXyJN8O/6IuFiLiY1dWvOM+vXr7c+s+a8WfBCVXggdzfShffaunWr1bWS6WbKdCNkuoW64447ci/MMN2fPPDAA9aFF+Hh4VY3KEVd7FXwwoGiLhIzzMVRI0aMsC7GMBeD9OvXL/fCpdJchGMu+jIXXZmLSI7X/dall15qXcRhph08eLC1rUV1v2W6gyqq+628Cm5zwbK6LlYxj02bNrUuejAX9uS9YMxctGHWabbbdI1luoTKe+FaUeswF6CY7TDb0KRJE6v+Tdlc3ScZptsnc5GJr69vbvdbpp7M+s1FG8V1v1XU/inIlMl0X2S66jLbZLoBy9vFTkxMjGPkyJHWhTWmmyozzlUPLmbfjRo1yprf7I+zzjort1upgtv8+++/W9NNnTrVem26JjIXMZljs0ePHo6PP/7YqndzkUzeei/I7E/TrVpx3W+Zi2vMhXWuLs1++OGHIrvfyqvgceFahrmw0HR5Zba9pGPSXKjWvXt3q67q1avniIyMzN0O10U+5gIkc1GO2d6C3S6ZC/PMRVOmngMCAqx9ceWVV+Yuw/j888+tLp9c480x52K6ezOfc3PxU97ut8xFiXmVZp+a+fIegwWPWfP5MhdHmWPdDKb7K7P9Zek2K6/ydL9lvPLKK9bn0mxXcce9OQ67du3qKIk5Ls25xHwezbFsui98//33c9dfVH3mPU+Y48t0L2WOS9O91vTp0/NdrFlwe4rapqLOEwXXW9bzbFFMWcxn1XUOMfvTdC3munDzeOVwnbvzXohqzjsFu4IrGAKZi8XMMWw+V71793bMnz+/xHLCM/iYP+4OpgGcOJNpNXcd89Q7olUXJmNmspLm583StPezC9MkwPySYNpKHq8NpruZbrjMBXYmw2rKDACefdYCADcx7RZNG0TzE6X5Odb8lG5+Fq5OQazdmOYq5qd4glgALgSyAFBM9s+0IzRt9MwFWqbHhhdeeIG6ciPT1toMAOBC0wIAAADYEr+RAQAAwJYIZAEAAGBLXttG1nS0bdq9VRXTGXbe246COuE44bPDuYTzK/9zqg7/h+1ZJ6b/aVPO4nhtIGuC2Krsrmj69Onl7vS+uqFOqBOOEz43nEs4v/I/x32m2yA2iYiIKHE8TQsAAABgSwSyAAAAsCWvbVoAAAC8Q05OjvWYnZ3t7qJ4nGw314mPj0+5bjRDIAsAAKptAGtuamJucGKujdm0aZO7i+RRGnpInQQHB6tly5YnFNASyAIAgGopJibGCo7at2+vpKQk1apVy91F8iiHDx92e504HA7t2bPH2leNGzcu8/wEsgAAoNoxAVJCQoJatWolf39/K6D18/Nzd7E8iq+H1EmjRo20Y8cO69E0NSgLLvYCAADVMpA1Q0BAgLuLguMw+8i1v8qKjCwAAKh2iguK0jKzlZntvPirIgX4+So4wP3ZTTsjkAUAACiGCWJP/e8MHUwq/k5RJ6phWJBmP3A6wWwVIyMLAAC8gsnEmiB2/kNnqGZQxYVAR9KzdMrz/1jLL01Wdtq0aXr22Wetrq/M7VebNm2qP//8U+eee67eeOMNtW3bVtdcc43++usvNWjQwJrmtNNO05tvvmn9DG/a/aakpFgXSbmaTvzzzz8aNmyY7r33Xr388stFrnfr1q168MEHtWTJEtWsWdNqI3vHHXfohhtusNbXt29f3X777cct/4QJE/Tjjz9aPUKsXr1a3bp1O+48n3zyibWOn376ydrOikIbWQAA4FVMEBsWHFBhQ1mC4v379+vmm2+2gtkVK1Zo/fr1eumll6yLnH799VcriHUxQaeZZvny5Vq1apXeeeed3HEtWrSwgkmXDz/80ApES1rv4MGDddZZZ2n79u1WAPrDDz8oKyurzPU3ZswYzZkzx+oyqzSio6M1efJknXzyyapoBLIAAABVZN++fVYvCvXr189976STTrICWZNpXbNmTZH9rJ566qnauHFj7nvXXXedFbwaiYmJWrBggUaOHFnseidNmmQt48Ybb8x9r27dulZQ7bJu3ToNHz5cHTp00MUXX6yMjIwil2WywxEREaXe5ptuukmvvfaagoKCVNEIZAEAAKpIz549dcopp1gZ1YsuusjKxpomAiWJj4/X9OnT1adPn3zB5LZt26x5v/rqK0VGRpbYldbSpUut9ZbEZH/NT/8mS3zgwAFNnTpV5fX222+ra9euGjBggCoDgSwAAEAVMe1STYA4b948K4M6d+5cK9DbsmVLoWlfeOEF9e7d22r7Onr0aKuNaV7jxo2z2p6azKzJ0JaXycKGhIRYAXH//v2tNrXlYZowvPfee3rqqadUWbjYCwAAoIp16tTJGsaPH28FtHnbu+ZtI1vSxVfXXHON1SzBNAUwdy8r2I7VFRz//fffVjZ3/vz5uvvuu4tdnmnC4GKC2bK2nzUXp913333Wc5Mhbt26tfbu3avOnTvnttO9/vrr9cwzz+Rr4lAeBLIAAMCrmF4G3LU80xTA3MVq0KBBuc0GTOYy70VepdW0aVM9//zzVkBc0Lfffpvv9a233qpevXrpo48+0rXXXpu7bvP6rrvuUkUw7WtN84S8Lr/88tznQ4cOtQLdiuy1wOsC2aioKGtITU11d1EAAEAVMjctMP29mq6yKppZrln+8Zgsp/mp3QSvoaGh1uurr75aF1xwge68884yr/fao0Hp8TRp0sTqacBkec36w8LCrGYOpiut4zHZYjO8//771uvbbrvN6vHAZFhN8Gq68iqqaURV8HGcyG0UqgFztZ3pDqKqmEbaI0aMqLL12QF1Qp1wnPC54VzC+bWymD5aN23aZP3sbn4mN1f2165dmzt75eGqE0/bV2WJ17wuIwsAALyXuWEBt5KtPui1AAAAALZEIAsAAABbIpAFAACALRHIAgAAwJa42AsAAHiPzDQpO6Pil+sXKAUcu6EAqgaBLAAA8J4g9vUe0pEDFb/smo2kO1eVKpidNm2ann32WavbqfT0dOvGBn/++ad1o4A33njDujmCuWuXuVNWgwYNrGlOO+00vfnmmwoICFCrVq2UkpJi3VzBvDb++ecf61a29957r15++eUi12tuOWv6kV2yZInV96vpR/aOO+7QDTfcYK2vb9++Jd5JzMWs39wFzHUnsIceekiXXHJJkdOadZl1pKWlWYPp9/aBBx5QRSGQ9SbJsdLU66XaEdIFb7q7NAAAVC2TiTVB7N3rpKCwiltuepL0Whfn8o8TyJqbCNx8881avHixWrZsab23bNky+fj46Ndffy3yFrUmADz99NP1zjvvWEGh0aJFC+smBaNHj7Zef/jhh1YgWtJ6Bw8ebN0MwdwYyjB3GPv9999PaJPNncO6det23OnMrWiffPJJnX/++YqLi7PuQmYC9i5duqgi0EbWm+ycK22bIa386th7896QPh8txW51Z8kAAKg6JogNrlVxQxmC4n379snf31/169fPfe+kk06yAlmT6VyzZk2heUzm89RTT9XGjRtz37vuuuus4NV1Y4MFCxZo5MiRxa530qRJ1jJMYOlSt25dK6h2WbdunXWnLnNjgosvvlgZGRXTBCMhIcF6TE5OVmBgoOrVq6eKQiDrLbbPlqZcJTUscD/m5V9IW/5yBrkAAKBS9ezZU6eccoqVUb3ooov00ksvWU0EShIfH2/dDbNPnz6575mmBtu2bbPm/eqrrxQZGVnorlh5LV261FpvSVasWKGffvpJ69ev14EDBzR16tRip73iiivUvXt3q1nCwYMHi53uo48+0qOPPmptrwmQn3/+eTVu3FgVhUDWW6Qckpr1kS7/xt0lAQDAa5l2qSZAnDdvnpVBnTt3rrp27aotW7YUmvaFF15Q7969rbavpgmBacea17hx4/TJJ59YmVmToS0vk4UNCQmxAuL+/ftbbWqLMmvWLK1cudJqEmEyy1dffXWxyzSBuhl27dqltWvX6uGHH86XWS4vAllvycb+eKcUnOd+yjvnS6u/lTJT3FkyAAC8kmkrOn78eH3//fc6+eSTrfauBZk2ssuXL7cCxscee8xqfpDXNddco4kTJ1pND9q3b59v3JgxY9SrVy9riI2NtbK58+fPL7FMrou3DBPMZmVlFTmdya4a5kKzu+66S7Nnz7Zem4vTXOs0F7MdOnRI3333ncaOHWuNb9OmjQYMGGAF8RWFi728wYG1UpMe0pgPpLREKSdL+niUFFxH8g+W6jgbmwMA4BXMxVluWp5pCmAusho0aFBus4Ht27dbPRWUVdOmTa2f6k1QXNTFWHndeuutVoBpfuo3PQe41m1em2C0tEw718zMTNWpU8d6bZo1mKyxYdrXmuYJLqZXBhMcz5w5U0OGDLECW9OWl14LUHYmGxtS1xnIGo4c6fbFUo0G0mcXSwvflZr0cga8AABUR6avV9NNlulhoKKZ5ZrlH4fJcpqeA0zwGhoaar02P81fcMEFuvPOO8u82muPBqXH06RJE82ZM8fK8pr1h4WFWc0cJkyYcNx5TbbYDO+//77VdtY0czBBqsPhsLKsn376aZHzmazulClTdM8991jbaQLg++67T/369VNFISPrbUz29dz/SX4BUujRKyZHPi99f6u0aTqBLACg+jJdY5m+Xt14QwTT5Za5cKsoJlPr8vHHHxe7jLzT5fXEE0+oJKb5Qd4LuExvB7Vr1y5yfXn7ojVdZ5nBMIGrae5QWiZLay40qywEst7GtK/pW+DbW8OOUoMO7ioRAABVxwSb3IGr2uBiLwAAANgSgSwAAABsiUAWAABUO66uqswFSfBsrn1UsHux0qCNLI6JXiQl7pFqN6NWAAC2Zq7IN/2cmj5UTaf9OTk51pX2OMYT6sQEsWYfmX1l9llZEcjCqfsY6bcHpE/PlxJ2Sd0jpQvfonYAALZlOu43d5SKi4tTamqqddcqHOMpdWKCWNdNFsqKQBZO7YZJnc+X5rzqfB2znpoBANhaYGCg2rVrZ2Ue//zzT6srKBxj7sTl7joxzQlOJBPrQiBbjX21aJe+nD5bT/t9rWbhDdXQ3QUCAMANXIGS6aAf+dm9TrjYqxpbvD1Ot9acpYaOOH2ac5a7iwMAAFChvC4jGxUVZQ2mXYg3CK8VpMO1TtamQOd9kEvUtJdUo6FUO6IqigYAAFAuXpeRjYyMtO776wmNmz1Olwuk+7dIg+9xd0kAAACOy+sCWQAAAFQPBLIAAACwJQJZAAAA2BKBLAAAAGyJQNaLxCSl6cJJc3XbF8vcXRQAAIBy87rut7zZrtgUrdidoDV7Eo8/ccYR6fA+qVaTqigaAABAmZGRRWENO0rpR6RPL6B2AACAxyKQ9UIOSc/9ul4PfLtSKRlZRQeyYz+R0pPcUTwAAIBSoWmBF/Dz9dHcLbFKyci2XmfnOPTurG3W82sGtlaXprWKmdMh5eSYm1RXYWkBAABKhwjFC7RrWFPXDGyl2ZsPlX6mkHpSSqw0qV9lFg0AAOCEEch6AV8fH3Vu4sy6hocFacKw9rr3zA6qFeyvHIdDmdk5hWdq0E66cYYUu7XqCwwAAFAKBLJext/XR/ec2UF3DGsvX18f3fz5UnV45DfN21JEtja4uCYHAAAA7kcbWS8xsG19XXlyC3VvVjvf+9Hxqdbj7vgUN5UMAADgxBDIeom6NQL1zIXd873XuFawktKyVDskwG3lAgAAOFEEsl7s+9sGKT0zR7d/xZ2+AACA/RDIerHgAD9rAAAAsCMu9kIJfJx9yf7xiJRdxI0TAAAA3IhAFsWrHSFdMEma94aUGkdNAQAAj0Igi+L5+Eg9LnE+X/ud5DA3twUAAPAMBLI4zhHiLw26U5r+sLR/NbUFAAA8BoEsLD+s2KvdcSlFZ2XPfOrozRHIyAIAAM9BIAvdcUZ7HTqSru+X76E2AACAbRDIQv1b11P3ZnWoCQAAYCsEsgAAALAlAlkAAADYEoEsSnmkBEi/3Csd3kuNAQAAj0Agi9K5Iko6vI8uuAAAgMcgkEXpNOkhhXBBGAAA8BwEstVUcnqWDqdllnp6Xx/p+xV7NH9rbKWWCwAAoKIQyFZTD0xdpdmbD6l+jcBSTX/n8PZqWidEv6ymDSwAALAHAtlqKiElQ89c2E2t6tco1fQRdUPVvVntSi8XAABARSGQRb670UYtiVbHR37Tkz+tpWYAAIBH83d3AeA5rj6llWoGBWhvQqqW7Yx3d3EAAABKREYWucJrBeuWoW01qF39omvFL1Ca/n/SAbK1AADA/QhkUXqRH0kBIVL0YmoNAAC4HYEsSq9uK6lGODUGAAA8AoEsAAAAbIlAFgAAALZEIAsAAABbIpAFAACALVWLQDY+Pl7XX3+9mjdv7u6iAAAAwFsD2QkTJqhVq1by8fHRmjVr8o3bvHmzBg4cqA4dOqh///5at26d9X7dunX1wQcfqGPHjm4qdfWzOz5VXy/aVfTI1d9KcdurukgAAACeHciOGTNGc+bMUcuWLQuNGz9+vG666SZt2rRJDzzwgJWFRcUb0iFco7o31v/+2lx45Gn3Symx0qpvqHoAAOBWPg6HwyEPZLKyP//8s7p162a9jomJsTKxhw4dkr+/v0yxmzRpogULFljTGsOHD9dff/1V5PJeffVVa3BJSEjQ1KlTq2hrpLS0NAUHB1fZ+l5bka17gn/SmfFfaWvEGG1tHlmm+bcmOvTu2my9OLDwXYy7bnlLYSm7tbbteCXVcNa9HerEDqgT6oRjhM8N5xLOr/zPOcYkLaOjo1Ush4dq2bKlY/Xq1bmvlyxZ4ujcuXO+afr16+eYOXOm9Xz8+PGOZs2aWY9btmw57vLNtFXp999/r9L1Xf7efMf2d69wOH7/P4cjK7PM8y/ZEecY8OxfRY/ct9rheG+Yw/HjnbaqEzugTqgTjhE+N5xLOL/yP6f08VrhdJsHM+1m88qbTH7nnXfcUCIbCKkr+VXwbm7cTeo4Spr9ilSvjTRoQsUuHwAAwI5tZItjeiQwqeWsrKzcIHb37t1q0aKFu4vmnU65XTrpamnnPHeXBAAAeCnbBLLh4eHq3bu3Pv/8c+u1ad9q2sa62seiYgX5++rQkXTd9uWyoifwD5TqtabaAQCA23hcIHvbbbcpIiLCyr6ai7fatWuXO27y5MnWYC76euGFF6wut1A5ujWrrTcu661fV++jigEAgEfyuDaykyZNsoaimH5i58+fX+Vl8lbdI2q7uwgAAAD2CWQrW1RUlDWkpqa6uygAAACoTk0LKltkZKSmTJmikJAQdxcFAAAA5eB1gSwAAACqBwJZlMh01fv+7G367+8blJ6VnX9kjQbSpt+l72+lFgEAQJXzujayKLtnfllvPY7q3sTqzSBX14uk1Hhp6SdUKwAAqHJkZFGsQD9fmZup+fn6yDf/TdWOqRHufExNkDKSqU0AAFBlyMiiWOG1gvXT7YOtYHbcB4uKnzB+u/RyBym0vnSvM3sLAABQ2bwukKX7rbLJ15SgOGmJzsekvSe0TwAAAE6E1zUtoPutE5eSUeBiLwAAADfyukAWJ6Z5vVBd9t4CrdlzNPsKAADgZgSyKJWpN5+idg1rKiYpjRoDAAAegUAWpeJ/tAcDAAAAT0Egi/Kp20oKDJMadnK+/udZKYe2tAAAoPIRyKJMpi7do/jkjGNvNO4m/V+0dOsCadTL0qz/SkdiqFUAAFDpCGRRavec2UErdifo97X7C4807Q76XkdtAgCAKkMgi1I7q2tjtQ2vSY0BAACP4OuNN0QYO3asUlNTZUtrpkkfjpQ2/yWPtWeptCqKtrIAAKBSeV0ga/sbImz8Tdo1X9r8hzzWN1dI026Qtvzt7pIAAIBqzOsCWVQhB70XAACAykMgi8oz4znp4CZqGAAAVAoCWZTZDyv2aGdsctEj/QKdj2M+dLaR3TmHGgYAAJWCQBZlcuewdopPztR3y/cUcTT5STf8JV39s9RttFS7GbULAAAqjX/lLRrVUZ+W9dQjorYOHE5TWma2ggP88k/QpKe7igYAALwMGVmUWY/mdfTVot3q9OjvevH3DdQgAABwCzKyKLNxJ7fUFf1b6I1/tmj1noSSJza3q83JkXz5zgQAACoW0QVO7MDx9VG9GgElT9SomzTzv9Li96hlAABQ4fy98c5eZrDtnb3sZPjjUvJBKSXW3SUBAADVkNdlZG1/Zy+78fWXVn4lbZru7pIAAIBqxusCWVSx0//P2cRgw89UPQAAqFAEsqhcNcOdgWzaYSmT5hwAAKDiEMiiXKLjU7Vxf1LJE4V3dmZkf7mX2gYAABXG6y72QsU5pW0DfbNkt8Z9sFCZ2TlqGBakP+4eUnjCbhdLqfG0kwUAABWKjCxOWLvwmrrjjPaKSUpXfEqmNh04UvIMh/dKK76S9i6n1gEAQLkRyKJqtBoshdaTFr4jfXW59VZ47EJp8fvOGyYAAACUEU0LUDUadpSu/lHas0z68hLJ4VDvTa9ImyQ16S1F9GFPAACAMiEjCwAAAFsikIUbOKyMLAAAQHkQyKJcIuqGKNDfV63qh5ZuhpC6Ulqi9MZJ1DwAACgXrwtko6KiNHbsWKWm0jl/RejatLY2PXO2vrzx5NLNUK+1dMt8KX57hawfAAB4L68LZCMjIzVlyhSFhIS4uyjeq0E76ZpftajrE1LNRu4uDQAAsCl6LYB7tBqk+I3H6XcWAACgBF6XkQUAAED1QCALAAAAWyKQRYUI8POVr4902bsLlJaZTa0CAIBKRyBrM8kZWdZjSqbz0VM0DAvSj7cP1vxtsUpMzXR3cQAAgBcgkLWZDfuSrMfV0YnyNJ2b1Mp9vj8xTXO3HFJODjc+AAAAlYNeC2zGYe6K5eHe/GeLPluw03o+pk+EHh7VWXVrBBY/w+E9kvpUXQEBAEC1QEYWFXcw+UhPX9jNerz6lJZ64eLumr81Vh/P21H8TE1PkqKulnbOk+J3SHP+J8VtY68AAIDjIiOLCuPj46NxJ7fM997yXQkl55Av/1qafJqUGi+tmiIt/UiK3SJd8CZ7BgAAlIiMLDyIK+T1/OYTAADA/cjIwv1C60vfXi/VbubukgAAABshI4sqkZ3j0D8bDig6PqXwyLGfShF9nU0KAAAASsnrAtmoqCiNHTtWqamp7i6KV5m2LFrXfbxEE75aXnhkUJgUWMMdxQIAADbmdYFsZGSkpkyZopCQEHcXxSv4+flYQezva/Zbr9OzcqxHh8OhH7Zl68Gpq5ScXuDmDrsXS9tmuqO4AADARrwukEXVunt4B3VqHKa/N8QUGvfrToe+Xrxbm2OOHHuz52VS7Qhp3sSqLSgAALAdAllU+q1ru+S541fxR+LR6w7rtpY6n8deAQAAx0UgC88w8gU5LnpXaX3H0/kWAAAoFQJZVLpB7RropBZ1NLxzePET1Wmud+L7qtMzc/XvxoPsFQAAcFwEsqh0A9rU17RbB+mSfi1y3zucVuACL0m74pxdc8Ulp7NXAADAcRHIokodTErXL6v2afCL/6hWoBQcUMwhmJYoJR9i7wAAgGIRyKLK9G1ZVz2b19FtXy5TUlqWXjjFT2HBAYWm2xvcTorbLn11qZSRzB4CAABFIpBFlalbI1CPntNFgf6+Cg8Lko9P0dPtDu0iXfW9FL1Yeq6pFj17pnKys9lTAAAgHwLZamjR9jhFx3vmncta1A/V4oeHa8Z9Q+VbXCRrNO6uNddv19UZ/1H/zEXKySGQBQAA+R3tvBPVycPfrVbzuqFqFBYsT1Q7pHBzgiL5+mldTsvKLg4AALApAtlqyCHpxtPaKGSNn+xo44EjGvP2PDWoGeTuogAAAA9GIAv3HoC+Pnrk+9WaeGnv3PdW7k7Ifd7QTeUCAACejzaycKv3ruqr/YlpOuOVmfpq0a5ip/PZNF1a+omUk1Ol5QMAAJ6LjCzcqluz2pr/0DB9v3yP/lh3QObyL/NoejbIyMqRv5/zgjC/qCudMzTqKkX0dW+hAQCARyCQhdsF+Pkqsm9zaziclmndMOGUNvWtO32FZsRK3+aZ2GFaAOcRs0H6+S6p7TBpyP1VXXQAAOBGBLLwKLWCA3RZf+etbFs1qKFD+4/TjdjuhdKu+c47gRHIAgDgVbyujWxUVJTGjh2r1FTP7GcVx7F7gTT7VWfgCgAAvJrXZWQjIyOtISIiwt1FQSnkhNTXW1nnq0ujGuqb+Jtq/vGIc8TeZdI5r1GHAAB4Ma/LyMJeGtYKVfDIp7Ss493akVlPDnM52KA7pf2rpYXvuLt4AADAjbwuIwt78fHx0XWDW1vPhy5+ShNHd1CPTh2l9KSjt34AAADeiowsbCPVJ0RZoeHuLgYAAPAQBLIAAACwJQJZAAAA2BKBLGzl8/k7lZSW6e5iAAAAD0AgC9t45JwumrX5oOZuOSQFhUkLJ0tLP3Z3sQAAgJsQyMI2zuvZVM3qhOjDuTsU3fNOqcdYZ3+yAADAKxHIwlYeO6+r4pIz9NGiA8o84wnp/DelkS9KqQlS9FJ3Fw8AAFQhAlnYSp+WdRXZJ0JfLdql9xYdlE4aJ3W9SKrXWvrqUncXDwAAVCEC2WoqYv370qbfJP9gVTfjh7TVRb2bKTk9y/lGWCNp5AtSztHXs16Spt4ofX+blHzIrWUFAACVh0C2mqq3d5bU6wqp/03yOjOel2o1kbbNkL67WTq8190lAgAAlYBAtjpr1E3yD5TXyEyVVn7jfN73eumCSVLcVmndj+4uGQAAqAT+lbFQoMo16CD1vlL6Lk8Guu3pUngXdgYAANUUGVnYUnCAn75etFvfLY92vhEQLA35T8kz7V8tTRogTX+4fCuPXiLNflVKjS/fcgAAQLkQyMKW7hreXqd1aKi5W2KPP/GhTdLnY6R3BkuJ0dKWv8q3chMI//2ktPyL8i0HAACUC4EsbCksOECtG9Qo3cRLPpCOHHD2OXv+xMouGgAAqCIEsvAO7YY5+5wNqu3ukgAAgApCIAtbiz2SrsTUTOeLoJpSk17O3hpC6hY/k8Mh5eRUTAF2LXA2WZhLphcAgKpGIAvb6h5RW0t3xuuhaaucbwSESONnSrfMlYJrOd8b8oA05EGp3w3O1zUaSLGbpQ+GV0whds13XkS2+Y+KWR4AACg1AlnY1ukdw/V/ozrrUFKGUjKO3tWroCY9pdMfkmpHOF837SVd84sUu8V50dfG36q0zAAAoOIQyMLWWjWooeW743Xjp0tKP5MJatMSpc9HS19dKj3VQIrfIe1eJP3+kHRoS9kLErtVeqGF9MFZZZ8XAACcEAJZ2NrJberr3XF9raxsqdVpId27Ubp/m/TAdmdzg8P7pHkTpQVvScs+KX7ezDQp52ib3LyS9jqD4wNrT2xDAABAmXFnL3insMbHnvsGOJsamIvAjKR9zou4vr9VanuGdM7LzvdnvST984zzea1m0p4l0qHNbig8AAAwyMgCrQZLP98tbf3H2evBxt+lzy6W4rZK22ceq5+ZL0lDH5L+b58U0U9a+50UVEs67f5j02QkO3sxMPOnJ1G3AABUIq8LZKOiojR27Filpqa6uyjwFBe9LTXtLWWmSD0ukU69W8pMLnra7pFSYOix181Okuq3cz6v21oa9pjUbri0fZaUtL9qyg8AgJfyukA2MjJSU6ZMUUhIiLuLgurCL9D5GBAqnXqPNPwJyZdWOwAAVDb+2wJGUNixx6wyZuu7XChdFio1aJ///bjt0t4V0sZfpNMfLjweAACUC4EsYIx+X4rbJjU9SZr/xtE68TlWN/tWSY7souvK11fqODL/e60GObv28g92NlMwyyWQBQCgQhHIAkZoPedgmLuA1Qh3tpld+I6Udlh673Sp+QCpZnjp6uvKqdLH50o75kg18/SQAAAAKgyBLFBQYA2p12XOXgyMnCzncEWUc5wx6E6pbkvppKuLr78rpzmbKfx0F3UMAEAlIJAFjufw3sLvmd4KzFDipyvQOQAAgErhdb0WAGVyaJP0ziCpcfdjvRMAAACPQEYWKI65OUK3MVLDjtKQB6gnAAA8DIEsUBxz8deYD6gfAAA8FE0LUC1kOxzKzM5xdzEAAEAVIpCF7TUMC9KWmCNq//BvWrgt1t3FAQAAVYRAFrbXrVltLXhomAa2ra/L3lvguZnZ7Cxnl15JB9xdEgAAqgUCWVQLjWsHa+JlvZXjkBwOeaalH0mfXSR9f4u7SwIAQLVAIItqZ+vBI/JI5k5h1mOqu0sCAEC1QCCLaqNmkL/6tKyr896Yo5ikNHmUuK3S5j/dXQoAAKoVAllUG8EBfpp6y0CZlgXpmR7WTnbpx1JqgtTvBneXBACAaoNAFtXSsl3x8jitT5OaD3B3KQAAqDYIZFHtXNCzqe6LWqnV0YnyCK0GSXVbSy2OBrFpidJ3t0gz/+vukgEAYGsEsqh2Xr2klxrWDFJGdra7i+JkmhPcuULqcoHzdcxaaeWX0pzX3F0yAABsjUAWAAAAtkQgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEsqq1tB5PdXQQAAFCJCGRRLZ3cpr7+77vVmrXpoDxWTraUsMvdpQAAwLYIZFFt7+7VpWltHUnPkkdp2FEKayJ1OFuq21KaNEDKIHMMAMCJ8D+huQCcmCY9pXs3OJ+nJ0nPR0g5HhZsAwBgE2RkAQAAYEsEsgAAALAlAlnA3RKj3V0CAABsiUAWcBe/IKlxD2nyEIJZAABOAIEs4C7+gdLNsyW/QHouAADgBBDIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGypWtyiNj09XePHj1etWrWUnZ2tSZMmubtIAAAA8LaM7IQJE9SqVSv5+PhozZo1+cZt3rxZAwcOVIcOHdS/f3+tW7fOen/q1KkaMmSIJk6cqLp162r+/PluKj0AAAC8NpAdM2aM5syZo5YtWxYaZ7KuN910kzZt2qQHHnhA119/vfX+zp07reDXaNOmjfUaMP5ad0CH0zI9uzJqhkvvnSEd2uLukgAAYCs+DofDIQ9kAtOff/5Z3bp1s17HxMRYmdhDhw7J399fpthNmjTRggULNG/ePGVkZOiaa67RY489prPPPlunnHJKvuW9+uqr1uCSkJBgZXKrSlpamoKDg8u9nNAlr+vUzLn6M3iEcno7A/mCnliYpa+Dn9PhJqdpb/hQeaqKqpPiLDqQo6lbczSiha/OiPC472y5/LNSdMqq+7W27c3aG9S+UuvEjir7OLEb6oM64Tjhs+NN55Prr79e0dHR9m8ju3v3bjVt2tQKYg3T9KBFixbatWuXRo8erVtuuUWrVq2yAtqCQaxxzz33WINLRESERowYUWXlnz59eoWsb8nad6VMqVZYmAYUs7yX1s5UrZBaatmtm7r3rrptdFedFMcsefenS9SpbX2NGNRaHm3r0+rXt6+mb06v0uPSDir7OLEb6oM64Tjhs8P5xIaBrCt4zcuVTA4KCtKHH37oplIBAADAHTz399YCmjdvbqWWs7KycoNYk6U1WVkAAAB4H9sEsuHh4erdu7c+//xz67Vp32ra0bou8gIAAIB38bhA9rbbbrPar5rs6/Dhw9WuXbvccZMnT7YGc9HXCy+8oA8++MCtZQUAAID7eFwbWXMzg+JuaNCxY0f6iEX1lRKrOoejpZzhkq+fu0sDAIDH87hAtrJFRUVZQ2pqqruLAuT3/a0akJUmtQyV+t9I7QAAYLemBZUtMjJSU6ZMUUhIiLuLgiry25r9Gvm/WZq2rPh+6DyCCWKNjGR3lwQAAFvwukAW3mfR9jht2J+kn1bu9dy7fLU/SwrvoqTQllLcVikjxd0lAgDA4xHIwit0blJLS3bE6z/frpJHOvsF6db52tV4hLTuB2nWS+4uEQAAHs/r2sjCOw3t2FCtG9TQjyv2ypNFNxquro0CpUzacAMAcDwEsqjWbjytjZrVDdHYvs21eEecu4sDAAAqEIEsqrV+repZg0EgCwBA9UIbWQAAANiS12Vk6UcWAACgevC6jCz9yAIAAFQPXhfIwnvVDQ3U/G2xuuebFfJovv7S+h+lT86TFr3n7tIAAOCxCGThNc7s0kjPXdRNq/ckyqMNulM6/WGpXltp9bfuLg0AAB7L69rIwrs1qhUsjxdaT+p9heQfJMWsd3dpAADwWGRkAQAAYEsEsgAAALAlAlkAAADYkte1kaUfWQAAgOrB6zKy9CMLAABQPXhdIAsAAAAvbVqQnJysuXPnKjo6WiEhIerZs6e6dOlSOaUDAAAAyhvI7ty5U0888YR+/fVXdevWTY0bN1ZaWpqefvpp+fj46L777tO1115b2sUBAAAAVRPIjhs3Tvfcc4/ee+89+fvnn23Hjh2aPHmy3nzzTd1+++3lKxEAAABQkYHsrFmzih3XqlUrPf/886VdFAAAAOCei722b99e/jUDAAAAVR3IvvTSS+VZJ4CySDssZaZSZwAAnEjTgpYtW6pjx47Wc4fDoY0bN+qtt94qzawAyuPQRumltlLt5tKEZdQlAABlDWTPPPNMvf/++7mvb7nlFtkVd/aCraTGOx8P73F3SQAAsGfTgpdffjnf67ffflt2xZ29YBsdR0nDn5BG5f/8AQCAMgSyderUKc1kACpSYKg0+G6p7RlSTpa05S/qFwCAE73Yy/QjC6CK1WoqdblQ+ny0lJZI9QMAcCKB7IoVK8oyOYCKEBAinT/R+dyRQ50CAFCe7rcAAAAAdyOQBQAAQPUPZE0fsoCdhQb6a0dssu78ermW7ozTgm2x7i4SAACoikD266+/PtH1AB6hf+t6ejmyp35YsVdj3pmvS99doL0J3DULAIBqH8g2atRIn332mZKSkqzXWVlZZGlhOx0ahVmPrh8YsrL5pQEAAK9oI/vss8+qZs2aSk1NVbt27dS4cWPNnTu3ckoHVIKGYUFqWT9UPSJqy8fHZlW8a6G7SwAAgH0D2Ro1asjHx0e//vqrdevaBQsW6KGHHqqc0gGVoEHNIM28/3T9ePtg+fsei2RXRyfqv79v0O64FM+rd/9gqevF0teXSTP/K2VnubtEAAC4nX9ZZwgKCtK+ffus9rLjx49X69atlZycLLuIioqyBpNRBoxVexK0eEecfluzX3+tP6DsHIceGtXZsyrH10+K/Eia20ua+ZIU0U9qe7q7SwUAgL0C2aefflq9e/e2mhScfvrpVhvZI0eOyC4iIyOtISIiwt1FgYe4/cvlso1Bd0rLPjMtfN1dEgAA7BfIDhs2TPv37899vXHjRiugBQAAADyyjezChUVfZNKxY0e988471k/169atq8iyAQAAAOUPZP/73/9q+PDh+uijj6wsbGJiopWZ/ffff/XAAw9owIAB+TK1ACpJaH3pm3HS7sVUMQDAq5W6acHUqVO1ePFiTZ482eqCKzo62urBoEePHho9erTmzZtndcsF2ImPfOzX3vSKKdJnF0mxm6Xm/dxdGgAA7NFGtl+/ftYAVBfvjDtJ+xPT9c2S3Vq5O0G2EFxbCgh1dykAALDfxV5AdXJGp0bW47dLd7u7KAAAoLJviAAAAAB4AgJZwM4cDulIDHf6AgB4JQJZwM4WviO93F7642F3lwQAAHsEsjExMZo9e7b1PCsrSxkZGRVdLgClcXhv/kcAALxImQPZadOmqX///ho3bpz1eu3atbrwwgsro2wAAABAxQWyzz33nJYuXaq6detar3v27KmdO3eWdTEAymveG9KCt53dcQEA4IXK3P2Wr6+v6tevn++9wMBA2UVUVJQ1mFvqArYVWFPaMVs67X4pKEyKXuLuEgEA4PkZ2bCwMB04cEA+PuaOSNKMGTNys7N2EBkZqSlTpigkJMTdRYHNPPPzOo3/bInik/O3Cc/Jcchheg+oShdMkq75RRryoOQbULXrBgDArhnZF198UaNGjdL27ds1dOhQbd68WT/99FPllA7wIJ8u2KmMrBxdNyhJA9rUV0pGlp7/dYM+W7BTl/Vvoecv7l51halRX6oxuORp5r0pHdoonfm0FFKnqkoGAIDnBrKtWrXSP//8o3nz5llZqIEDB1o9FwDeZuLfWzRlyW51a1ZL2w8dkceZ9V8pLVHqcoHUbri7SwMAgPubFpx11lmqXbu2zj77bCszW6dOHes9oDrZEpOkCyfN1cvTNxY53jQnOJKeqatOaanrBrWu8vIBAIAyBLIm65qSkqKcnBzrQinz3Az79u2zHoHqZMXuRK3YnaDf1uwrcvyL0zfo8wW71LQOba0BAPD4QPbZZ59VzZo1tXr1atWoUcN6bobOnTvriiuuqNxSAh5k2rI92rg/Sfee2UHXDGzl7uIAAOC1Sh3IPv7441Y29qabbrIeXUNCQoIeffTRyi0lUMn8fJ29cBQlKztHD3y7UpnZObpreHv9veGA/t14UCGBfrm9dwAAABtc7PX2229XTkkANzI9DizblaCtMUf00dwdatOwhvV+ZrZDexPSNGVJtN6+4iSd3b2J1uxJ1F/rY1Q7xAO7vcpMlX6cIPkHSjnZ7i4NAACeFcgeOnRITz75pFauXKm0tLTc9xctWlTRZQOqTLvwMGswWdf0rBx9PG+HGtcKVnxKhs6fNMeaZkjHhtbja5f00r7ENLUPr+l5eyhpn7R6irtLAQCAZ/ZacN111ykiIkL79++3mhSEh4drxIgRlVM6oIoF+PmqYViQ9dxkZb+9eaASUjLzTRMWHKAOjcJoVgAAgN0C2V27duk///mPgoODdd5552natGlWn7IAAACARweygYGB1mNQUJDi4uLk7++v6OjoyigbAAAAUHFtZDt27GgFsFdeeaVOPvlk6+YIvXv3LutiUIlOzZynkKxt1LGbJKZkKsfhUN0azi99AADAAwLZ7OxsNW3aVPXq1dOdd96pvn37Kj4+3rrLFzzHNelf6HBEHwV3GuXuoniNw6lZOnA4TbWCA3TaSzOUnpWtWfefrvBawVVXiH2rnL0WAADgJcoUyPr5+eXrnWDQoEGVUSZUgP1tRis8pC51WQU6Na6luOQMXf/JYn1xw8lKTHVeHPbn+gO6vH+LqrkobOs/0vofpQBnt2EAAHiDMreRNRd4vfjii4qJicm9TS23qEV11axuiM7s0kgXn9RMQf5+RU7TpWktvTK2p1LSj/XbaqZ/8qd1WrozvmoKmnHE+ZiZXDXrAwDAjm1k77vvPuvxoYcesjJNDofDejTNDoDq4KwujbRu72GN7NZYNYP89d5Vfcu8jKcu6GYFseaGCgAAwEMCWXNbWqA6a98oTJOuOMndxQAAABUdyNpdVFSUNaSmclEMAACAV7WRtbvIyEhNmTJFISEh7i4KUDXWfi+lH21DCwBANeJ1gSzgVU65Xdr8h7Tue3eXBACACkcgC1SQ5Iws3TtlhUxvW75V0ONWiYJrS74B0oDxUsNOzvdWfiM900ia8ZybCwcAgAe0kU1MTNTu3bvVrVu3CioOYE/+vj46cDhdR9Ji9c1Npyg00I3Nz00Ae/tSKSdTqtX02Psx66SsNOnAWveVDQCAClTm/7YjR47U119/LX9/f/Xs2dN676qrrtJTTz1VkeUCbKV/63qaPK6PmtQOVo+IOu4ujlSzobtLAACA5zUtOHDggOrUqaNff/1VF1xwgTZv3qzvv6f9Hbyb6Ut5RNfG7gliA4KPZWIBAPAiZc7IZmY6b785a9YsKzsbEBBQNbfgBFC03ldJtZtLaYnS1OupJQCA1yhzRta0hzUB7M8//6wzzjjDuj0tgSzgRn7+Uvsz87eHBQDAC5Q5I/vf//5Xy5Yts9rHhoaGas+ePbr33nsrp3QAKsb6n6SQusdeZ2dK89+UajWTeoyllgEA3pGRvfDCC62hdevW1utmzZrptddeq4yyAagIAydIhzZLK7869t6eZdJfT0jf3UwdAwCqfyCblZVlNSPIycmxbu9qnpth37591iMAN/MLcj76H310aT9ciuhX8rw/3yN9OFJKjK688gEA4K6mBc8++6yefPJJqz1sjRo1ct+vVasWTQsATxDRRxr3vVSjFF1vmT5m81o7TUqNl2LWS7UjVKky0yS/QMmX+7EAAMqn1P9JHn/8cSsbe9NNN1mPriEhIUGPPvpoOYsBoEK0PV1qfJwblJhmBZ+cL9VsXPWVvuIr6dlG0u8PVv26AQDVTplTIm+//bYVwO7du1e7du3KHQDYRNJeZ7B79Y+SHFLctmPjTBde0UuknOzSLy87yzlP2mFp3yopaX/x08Zvz/8IAEBV9lrwySef6I477rDu7OXn52e9Z5obxMTElKccAKpCeFcpLUFqdaoU1kRq1FWadPKxpgY/TpAyk6WRL0gn31K6ZZreD/563JnhPbJfqt9eumNJpW4GAAAnFMiaW9EuWrRInTp1ogYBu2k5UDrn5WOvb/hbeib82GsTxBrpSSUvx/SCsH+11Pm8Y9OaILY08wIA4K5AtmHDhgSxgLebeoO0b4V0wVvO180HSL7+Uo0G0q6F7i4dAMBLlLmN7MUXX6w333xTcXFxuV1w0f0WULLsHId2x6UoIyunelRVTtbRx6NNEsI7S9f+Kg28063FAgB4lzIHsg8++KAmTJigBg0aKCwsTDVr1rQeARTv3VnbdOp/Z+i5X9dTTQAAuCuQzdv1VnZ2du4jgPxCAvx0/7crtelAkg4dSbfecz3aRlaGtOg9acecss23+ltp/iRp2WfmpFFZpQMAeLkT6pHc9FAwe/bs3Dt+ZWRkVHS5ANv78Jp+qhnkr/X7DstjmRsTDLpLOulqqW6rwuM3T5d+vU+adlPpl2ku+pp6vbR7ofTLPdL+lRVaZAAATjiQnTZtmvr3769x48ZZr9euXasLL7ywrIsBqr2mdUJUKzhAHs3HRzrzSen8iVJAaOHxDkf+x7IY+6kUXLt0826bKa2ZemLrAQB4rTIHss8995yWLl2qunXrWq979uypnTt3VkbZAHiLzy6Uvr3O2RMCAACVFcj6+vqqfv36+d4LDAws62IAeDpzdy/TV2x57VspHTkobf5TyiqmjbDjaDta0542+ZBz2sy08q8bAFCtlbkfWdNDwYEDB6y7eRkzZszIzc4CqEZWfCEteEtqO0yKOcHeFjqOkn77j/Tn41J6orNN7vH8cq+07nvpjEel0+47sfUCALxCmQPZF198UaNGjdL27ds1dOhQbd68WT/99JPsIioqyhpSU1PdXRR4idQMG/bqYe7OFRQmtR4iDb5bmnLViS3HtL3du+xYZjf76IWhWWnOjGtAcOF5zLi8jwAAVFQg26ZNG/3zzz+aN2+eHA6HBg4cqDp16sguIiMjrSEiIsLdRYEXiKgXogenrVbjWkUEbFWpftv8jyWp01KaN1Gq0VCK6F/+dddrK+1fI9VpLiXskmq3kLbPlqaMk66IKv/yAQBeq8xtZNu3b68bbrjBCmLPPvtsWwWxQFV7aUxPjezaWPsPuzm7eNr90mNx0oCbjz/tqfc6u+RKPlgx6478WHosVmoz1Pm67VDpks+lpH0Vs3wAgNcqcyC7a9cunXPOOVYTg+bNm+uhhx7Spk2bKqd0gM35+frI38/ZntytTJt2Xz/nY2mmDazpfB4QUvx0fkHSjOekbf+Wbt353juhLqwBAMinzP9NatSooWuuuUYzZ860hkOHDqlz585lXQwATzbwDunKadKol4qf5tzXpEZdpT1LqrJkAACceBtZ1928fvzxR3300UdatGiRbrnllhNZDCrBx3O361RuCYryMhdhtRvmfB67tehpajSQajU98XWY43T+W1LN8BNfBgDAq5U5kJ0wYYKmTJmi3r17W5nZqVOn0o+sB3nlz006NzhALZrUcndRUIIj6Vny9/VRcECBn9ztpvdVkrkZV68ryz5vzFpp+kOVUSoAgJcocyDbqFEj685ezZo1q5wSodzCQgIU4AntMlGkzQeSdM7EOWpeL0R/33v0AihPZ25YUFR3WS0GOAcAADy5jeyGDRusx4cfftgKZvOaO3duxZcMqKYOHklXRnaO9ibYpJ9U04QgLVGa1K98yzHdeuV9BACgqgLZyy+/PPd5//75+5a84447ylsOoNq6tF8LXdS7mc7p0US2VK+1dMtcKWF3+ZZjuvV6aI/zEQCAqgxkTb+xRT0v6jWAYwa3b6DXLumlHs1q27daSnNr2eMx3XAF1SxdF2AAAFRkG1mfPP988j4v6jWAoiWlZSk6rhrfHrlxN+nU+6Sa+ZsfFdJqsLOLr5B60t9PVlXpAADeGsimpaVp/fr1VvY173PXOAAl69g4TG/O2KJlO+PVtWktbTuYXP2qzD9IGvbo8acLriWd9Yx0eB+BLACg8gPZlJQUjRo1Kvd13udkZIHjG9oxXKufGGE933rwiM6dOMc+1eYfLPkFSC+1d75OiZWCalVM4OtqtmC+GP92v7R/tRRQo/zLBgBUe6UOZHfs2FG5JQHguWo2lG5dIKUlHAtszV29yiu0nnTbQufzD8+W9iyVznpWOsRtrwEAlXRnLwBeqH7bylluvTb5X9dtJcVulpIPSVkZlbNOAEC1QCALwPM06ib9/pAUFGb6fXB3aQAAHopAFoDn6X+jsx1u4m6pwM3EAAAocz+yAAAAgCchIwvAqePZzp4JmvWhRgAAtkAgC8Bp2GPOAQAAm6BpAQAAAGyJQBYAAAC2RCALAAAAWyKQBdzEIYfSs7KpfwAAThCBLOAGtYID5O/rq8EvzlBaJsEsAAAngkAWcIOGYUGacd9QHUxKV0Z2DvsAAIATQCALuElIoB91DwBAORDIAgAAwJYIZAEAAGBLBLIAAACwJQJZAJ4rdptC0mLyv7drgbT+Z8nhcFepAAAegkAWgGdqO0xKPqhOOz7M//7H50rfXCHtXe6ukgEAPASBLOBmpgsuFKF5P2ng7fJxFOieLCfr6CP97wKAtyOQBdwk0M9X7cJr6uzXZ2tfYir7AQCAMiKQBdwk0N9Xf90zRP6+PkpOP5pl9GY+vvkf80o/In1/q/THo9bNfQEAMPypBgAeYcwH0sGNUvszC4+L3SKt+MIdpQIAeDACWQCeoeVA5wAAQCnRtADwAPEpmdq4P8ndxQAAwFbIyAIe4NqPFutIepZu7+6rEe4uDAAANkFGFvAAJog1krnmCwAA7wlk4+Pjdf3116t58+buLgoAAACqeyA7YcIEtWrVSj4+PlqzZk2+cZs3b9bAgQPVoUMH9e/fX+vWrStxWXXr1tUHH3ygjh07VnKpAQAAIG8PZMeMGaM5c+aoZcuWhcaNHz9eN910kzZt2qQHHnjAyrYa+/fv19ChQ/MNd911lxtKDwAAAK+92Ou0004r8v2YmBgtW7ZMf/zxh/V69OjRuv3227Vjxw4rg/vvv/9WcUmByhfk76uUjGzrxggAAMCmvRbs3r1bTZs2lb+/s1im6UGLFi20a9cuK5Atzs0336wNGzZYj/fff7/atm1baJpXX33VGlwSEhI0ffp0VZW0tLQKWV9oqvNWpoeTkopcXlZWllJSUrR66VLFbvXse9FXVJ3Y3Z3dHDqc4aufd+QoMzOLOskj4sBaNcjO0bz581Wwh9kFCxcocV2cvA2fG+qE44TPDucTDw1kXcFrXg7H8W9H+c477xx3mnvuuccaXCIiIjRiRNV1dGQCtopY35K170qZUq2wMA0oYnn+86crNDRUffv0kdoNlyerqDqpLuZOnq+AgATqJK+l+3Rw9iINPOUUaXX++jp5wMlS837yNnxuqBOOEz47nE88tNcC0/NAdHS0lVV0BbEmS2uysgC82OE97i4BAMADeVQgGx4ert69e+vzzz+3Xk+dOtVqUlBSswIA1VvdpI3SN1dKTXq6uygAAA/jlkD2tttus37aN9nX4cOHq127drnjJk+ebA2m+60XXnjB6loLgPfyz06VwrtIl0e5uygAAA/jljaykyZNsoaimP5g58+fX+VlAgAAgL143MVelS0qKsoaUo9e/Q8AAAB78qg2slUhMjJSU6ZMUUhIiLuLAgAAgHLwukAWgI20OlUxdU+Seo9zd0kAAB6IQBaA56rfVss7PSidfLO7SwIA8EAEsgAAALAlAlkAAADYEoEsAPtKTZB2zJWyMtxdEgCAGxDIArCvX++TPh4lzZvo7pIAANyAfmQB2FdGcv5HAIBX8bqMLP3IAgAAVA9eF8gCAACgeiCQBQAAgC0RyAKwpzmvSimx7i4FAMCNCGQB2M95r0t7V0i7F7q7JAAANyKQBWA/bYdJYY3dXQoAgJsRyAIAAMCWCGQBAABgS9wQAQAAALbkdRnZ6n5DhNO1WH6pXMkNL7NrvnRwk7tLAcCO4rZL/74oHdxYuumPHHROH72kskuGUvC6QLa6e0aTlBxxqhTRz91FAapGj0udt6id+z9qHEDZzZ8k/fucNPvV0k2//DPn9H89QW17AALZaiiu921ScG13FwOoGs36SJ3Po7YBwAsRyAIAAMCWCGQBAABgSwSyAAAAsCUCWQAAANgSgSwAe/DxLfq59dpH2jpDWjWlyosFwAss/UT68hIpZoO7SwJvvyECAJuq2VC69CvJkSPVbpZ/XP+bpEObpZVfST3GuquEAKqrFV9IuxdKrU9zd0ng7RnZqKgojR07Vqmpqe4uCoCy6jRK6nxu4fdNd3OtBlOfAOBlvC6Qre539gIAAPAWXhfIAgAAoHogkAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEs4CHCgv31zeYczdl8yN1FAQAUY3tssvW4Ky6FOvIABLKAh3glspdahvlozd5EdxcFAFCM/Ylp1mNSWhZ15AEIZAEPUTs0QDUD3F0KAADsw19eeItaM3CLWgAAAHvzuowst6gFAACoHrwukAUAACiLjKwc6zE7x0HFeRiva1oAAABQFjtik9VB0vLdCbnv+TsypdmvSJmpUp2WUu8rJR8fKraKkZEFYE+D75b6XCt1OsfdJQFQzbkSsVnZzsys0TFznTTnf1JitPTj7VKGs1suVC0CWQD21OV86bz/SbWbubskALxVzUbSyOfdXQqvRiALAAAAWyKQBQAAgC0RyAIAAJRTTFI6degGBLIAAAAnKCUj23o863+zdOCw8/a1qDoEsgAAACcoM9uR29dscnoW9VjFCGQBAABgSwSyAAAAsCUCWQDVS1aG9M8z0vxJkoPbSQKoOPWObFbr2JlFjrvF/0cph6YFVY1AFkD1cnCDNOslafr/SdkZ7i4NgGqkw74f5ePI0SXpj+q7mpdJZz0jBdXWI5nX6ha/HxVweKe7i+h1vC6QjYqK0tixY5WamuruogBFWrMnUQkpGcrOceinlXu1Yf9hagoAPMRq/25a6Oisb2pdrd8ze2rlnkR9nn2msuTn7qJ5JX95mcjISGuIiIhwd1GAQno19NFPO+L11r9b1at5Hd3x1XK1aVBD/9w3lNoCAA+wPzFNwzqF65+NMVqwLU4+Pu4ukXfzukAW8GR9w32lek2UmZ1jDUZmjvMRAOB+7cJrKqR7E/29IcZ6TVN89/K6pgUAAACoHghkAQAAYEsEsgAAALAlAlkAAADYEoEsAAAAbIleCwAP4+/nq+9XRGvHoWR3FwUAUAR/P2efW74+Ug43EHQrAlnAw9wytK3VT+F3y/e4uygAgCKc072JAvx8ZcLZW75YRh25EU0L7GL6w9Kkk9X1yHylO45+/zi0WXpnsPTrA1JOtvTlpaqhVOX4Bbu7tCiHWsEB6tq0FnV4IhJ2S9+NlwJCJR8/6fPRUgaZbcBrzHheemugtGfpiS8jNV768Gzrf65raJ25VRkOv3y/nI3q3sTqU9YlTYEKn36LlLS/vFuBMiCQtYtN06Uekfqk6WPWrfAs+1dJ+1dLm35z3lN+02+6RQ8rs05rd5cWcI/YzVL6EenmOdJNM6Qdc6TkQ+wNwFts/kOKWStFlyOQPbxX2rdCOuuZ3OH5Oo/r75yTSpxtdMYT8ks5KMVuOfF1o8xoWmAnTU/Shj111Emzi51knQhi4eWCakr12zqf+/BdHcAJ8A2Q2g/Pfbk6KFT99X2Js2x1NJPDP4TqrmKc5QEAAGBLBLIAAACwJQJZAAAA2BKBLGATMUlp2pOQ6u5iAABKkJKZrS0xSXI46GC2KhDIAjaQnpWtYS/P1NCXZhDMAoAHm/TPFg1/dZa+XRrt7qJ4Ba/rtSAqKsoaUlPJbME+snMcSkrPsp4nH30EAHieI+mZ1mNCivMRlcvrMrKRkZGaMmWKQkLoIgMAAMDOvC6QBQAAQPVAIAsAAABbIpAFAACALRHIAgAAwJa8rtcC21n3g5SeJKmE/ujSDkubpldlqQAAsKeNv0tJe6WTrpZ8/dxdGpQTgawnSz8iTbmq5GkadpbCO0lRV1dVqQDP1OZ0qdsYqfVp7i4JAE/2zZVSTqYU3lVqMcDdpUE5EcjaXVCYNOwxae137i4J4F51mktjPmAvADgO1y+cFX/nrczsHCKrKkYbWcADdWtWW83qhKhvy7ruLgoAoBROaVNfh46ka9vBZOqrChHIAh7o5Db1NffBM3TfiI7uLgoAoBTev7qv/Hx9lJFV8ZleFI9AFgAAALZEIAsAAABbIpAFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALeIEtMUf0xt+btTsuxd1FAQBbmbXpoPYlpLq7GCiGf3EjAFQfb83YomnL9+jgkXQ9dUE3dxcHAGzj/75brS5Na6lXeh1pr9QuvGbuuBb1Q3X94NaqGeSvkAA/Jbi1pN6JQBYAAKAEN53WVk0XBVuBbIOagbnvB/n76dFzu1B3bkTTAgAAANgSgSwAAABsiUC2KuxbqSFLxkvf3Vz6eVITpPfOkPxDJPnkG3XYEaqTDv0ozX294ssKVEe/PSi91F7aPrt8y8lKlyYPkSYNcH5GAdjX7+a80E7aMafCFnlENTQp8HV18dlRYctEybwukI2KitLYsWOVmlqFVyAe3KjgzHhp98LSz5MSK8Vvl25fJAUea1huvJt9rhY3vNgKkAGUQvRiKTlGOrC2fNWVmSLtWyEd3OD8jAKwr73LpeSD0sH1FbbI+4If1wFHXbXz2Vthy0TJvC6QjYyM1JQpUxQSYjKdHs7HV6rTotDbWfLX4YBwtxQJAAAULdGntpIVTPVUIa8LZAEAAFA9EMgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEsAAAAbIlA1qYysnJynyekZLq1LKhc2dkOHUnPyn19KCld+xPTrMHhcFjvHTicprTMbHYFAJRTQkqGcuTQ4bRM69ybneM8z5ZVUlqmdZ7Oyj72/xoVz78SlolK1rJ+qFZGJ+j8ACk1M1sXvzVP/wRS7dVRo1rBik/J1KjXZ+fu+8vfP3ZjjZcjeyrI31d3fLVcI7s21jvj+rixtABgb2v2JOr+9xbom4AsDX3pXwX4+SgpLUuNagWVaTkNwgI18Z8t1nDD4NZ65NwulVZmb0cga0OX9Guu3tGNpC1SVo5DWUezcqh+WjeooZ8nDNawV2Zar3+4bZBqBQdYz++eskIxSWkK8vezXpvnAIATF5ecoWZ1QlQz219xiRnWezPuG6qW9WuUaTmjujfRI6NG6Z1ZW7V+XxK7pBIRyNqUj4+7S4Cq4ptnZ/vIR76+ztccAgBQ8XwK/IM9esotM3Ouznv+RuWgjSwAAABsiUAWAAAAtkQgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlAlkAAADYEoEsAAAAbIlAFgAAALZEIAsAAABbIpAFAACALRHIAgAAwJYIZG0q2yfAenT4BijL4ed808dXOexSoGg+ftK8N6R3T5dWf1s5tXRos/T+mdLfT1WvvbBvlfTeMGn2K1JmqvTlpdLXV0hZ6e4umf1FL5XeO0Oa92bJ0235y3nsrviyqkpmP9Mflg5usP4Xlppr2oBQafN09fr9Yv1f6suSX2DhaV3vFTUuj0yHvzqvm2jtr4uXjFPfxD/KtBkoG/8yTg8PsbHZxVpzOFgDBg3T3p27dXnW4/py/GAlfRDv7qIBnmnkC1JYI2ndD9LmP6XuYyp+HXuWStGLpJRYadhjqjZ2LZD2LJEc2VL3sdKm35zvpyY46xQnbudc53HjFyQNvL346bbOkPYukzb9LvW6nBovyppp0un/J+2YW8bzQmOp5WBp5xxF7z2sqKXRujtypDR5b/5pRzwndTlfantGiYt8IPMmvdbVof6t6ylh5tfqlryA/VWJyMjayBcLdyo+JdN6nuMboC+TeumPPc5vhguyO+rd7fUlh5sLCY8RtWS3Xvhtg/5cd0ArohPcXRz3qxkudblACu/s7pIAOEG741L0xI9rNW/roaInaD6g2IxsjsOhyTO3KjvHUfi8UKO+9XioxUjNDhio7PCuhRdgvrSZaYPCSizjHjXUvmZnWdPG1WhXtg1EmZGRtZFf1+zX3JyGGtCmns7v1VQLtsXqrX+3qlmdEF0+oIWe+3WDu4sID/L0z+t0OC3Lej6kQ0P1aFbb3UUCgHKZtmyPPp63Q9sOJWtg2wZlmjc5U3r+3w26Ifj409YJDdDj53VRRlaOmtcNLfU6/m9UZ62KTtDpncLLVDacODKyNhUeFqwLejWznvv4SNcPbu3uIsGDndOjiRrXDnF3MQDA7Xx9fY47jY+Pj64d1Frjh7Qt1fQuI7s11gMjO6lWsPM6FlQ+AlkAAADYEoEsAAAAbIlAFgAAALZEIAsAAABbIpAFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALAAAAW/KXzc2YMUOff/650tLSVLduXb355pvuLhIAAACqa0Z2woQJatWqlXx8fLRmzZp84zZv3qyBAweqQ4cO6t+/v9atW1fisk4//XR98MEH+uKLL7Rz504lJSVVcukBAADgtYHsmDFjNGfOHLVs2bLQuPHjx+umm27Spk2b9MADD+j666+33t+/f7+GDh2ab7jrrrty5/v555/VpUsXhYWFVem2AAAAwIuaFpx22mlFvh8TE6Nly5bpjz/+sF6PHj1at99+u3bs2GFlcP/9998i53v//fcVHR2tF198sdh1vvrqq9bgkpCQoOnTp6sqNDm4Sj0kJaekaE4p1xmauk8Ds7P11/TpGpadlW9Hbd60WdPTt2rlgRzrdWpqqv7888/c8fPmztPOmj7ydKY5SFXtA7soqk4OpDhyn//9z9+qEeDct3v3Zcvn8H75+xZ9TGdlZeU+N798mOWkZ0vTp++RnZTnODnT4dDsWbPUMzFRO1au1IHoELWJ3qLQ1L1acwLL9M86omFHn8+ePVspIVvyjW9ycHWZP+t2+Ny02LdenSUlJh7WilmzNOTo+zP+/VcZgXXkbnY+l7Tau1EdJcXFx2txCdvQYccOtbaSOge0shTbauc6KcmW7c7/e4cOHSq0fUPS07Ri4UK1jjmguPT12pWQf3x6eroV9jgcDrn+Q66wzguhudOsi8tR8pGcCqu7jIOHVDc11WP3RVo1OE48qo3s7t271bRpU/n7O4tlmh60aNFCu3btsgLZokydOlVPPvmkzjnnHN188816+umn1bBhw0LT3XPPPdbgEhERoREjRqhKrEqUtkg1QkNLv87YrdIaP+f0S/2l7GOj2ndorxFD2ylj5V5p3XKFhITozDOHSLN+t8YPHDRQnRrXkqczH54q2wc2UVSdbD+ULC10fokbdsYw1Q4NsJ7/Fr9c7RuHKcjfT9qyTnXq1NGIEYNy5/OfP91Es9bzbt26KfRgslIysjRiRDfZSbmOkwU+GmK+OMd8rF49e0pdR0izVkmHpGYnsszUeGmx8+mpp54q1W+bf/zK+LJ/1u3wuVm4U9oh1a5dy1mfy5xvnz50qBTWSO5m63PJ3I3STqle3bolb8P0WdI+qXHjRmpcim21dZ2UYMNfm6Udm9SgQQONGNE//8g1wTp5wABpznw1atNZnQfk3/5vfzL/I7Ot2EJH8wO554WjQjYd1C/71mnECNfXtfJZcGCOAg7u12AP3RfTq8Fx4lGBrGEdYHmYb04lMVlbMwAAAMC7eFT3W82bN7eaCLh+EjVBrMnSmqwsAAAA4LGBbHh4uHr37m11p+VqNmCaFBTXrAAAAADeyy2B7G233Wa1UTXZ1+HDh6tdu3a54yZPnmwNpvutF154wepaCwAAAPCINrKTJk2yhqJ07NhR8+fPr/IyAQAAwF487mKvyhYVFWUNpssqAAAA2JdHtZGtCpGRkZoyZYrVZZUnMxe6Zec49P7sbcrIdvabB5TkwOF0LdwWW6pKyslx6OtFuzRjQ0yx0xxJz9KHc7ZrVXRChVd8ZnaOPluwU/O2HpIdZGXn6IuFOzV/a+nqtzyW7IjTx3O3Ky0zT597FexgUrp1btmw/7A+nb9DC0o4bg4dSbfNfoL9RMenWMfi7riU4067JyFVS3fF5863bFe8pizZrT/XHSh2ntSMbH00d7u1jll7C/eCFB2fao0znwnzmftrffHLOlFJaVlauzexwpcLL83I2oX5wDbIzrE+qFcc7YKsZb0ampvn/8mA1vV0fs+m6t2ijgL9fDV+SBsdTs2ypkP10bROsK48uYWC/f1UM7joj2yflnUVGuine6NWas5/zjjuMpfvTtCD01bL39dHW54bVeQ005ZF66mf16ln8zr64bZj/dNWhJkbD+rR79eoYViQFj88XJ5uwbY4PfzdGjULTtPcSl7XA1NXadvBZKu/4It6R1TKOj6cu11v/7s193WzOiGa+2DRx83Hc3fo8Nr9GujsvhioUJNmbNVXi3ZpS8wRvTDa3E6keG/N2KI98Sn67+ge+nZZtC5+a17uuM3Pnq2iDtGfVu3VpBlb1L91Pe0/4tDNQ9rm9gVtPPfrev2aU1uxyRlq27Cmflm1T3ef2aHCtq9do5pat8dHT/+8Tl/fdEqFLRfHEMh6KNf3xreu6CM95ydlSDWC/PJNE14rWBMv6537+qGzzb13UN2YGx48c2H3EqcJDwvStYNa6+5vVpTpCCupl+bcLpyP05fziajERVcKh6vE5qGyb5rnqPy6Kbjskvrrzt12oHKOxiKPyeKmPLtbE43t11wNwgK1aHtcqWbq3KSW9b/U2fl/p3yBbO5kDufnoFOTMF15cktVlAY1gtS6Qagc/LBaabyuaQEAAACqBwJZAAAA2BKBLAAAAGzJ69rI0v0WAABA9eB1GVm7dL8FAACAknldIAsAAIDqgUAWAAAAtkQgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlbogAAAAAW/K6jKxH3hAhO1OK3ynlZDtfJ+6Rb2ayu0sFVA+H90rZ6YXfT4x2fu7MkBLnfC89yTl9VsaxcdmZCkzeq1ClqY6Sjs2fccT6rOZyOKSU2GOvjxw8tty8cnKcyzXrMJL25y9DSdNKCsg8XPS0xTHTmrIYmWnO5TlyFOFzUAHKKjx92mHp8L7SLduqr2KmNfVoxpd1uanx0pEYKSNFSthdeJ9llPLcWJZpC5UhQUo6UMxy90jpR4oe5zpuzH4rcT+kSgm7nMdM7rxpx5ZbcFqzzPTDKpfkQ4WPm4L/ewruMxfXcWN9HrKc+8UqU5KUHOscCjLbZh27Rz97Zp+afVtg//o6sqxj0TwWyfWZLLBc3+x0hSvemreWitgfjmxrutCUPWqQ5fyM+WWlyB38s1LU8GgZlJaYf/+i3LwuI+uR/nxMWvCWNOwxqVkf6dML1Di4njLdXS7A7hp2kj4eJfkFSnVbHXt/8x/Sqq8lH1/nP8aQOtK9m6zPnvYslRp2lg6uz13GgIMb9GtgIzVRnFS3tZSwU/r0Qik1TrrqR6nNEGnlV9L0/5PqtZWS9kn/6yb5B0v3bpQCgo+te97r0l9PSANulnqPkyafZgWWCq7lnDav+W84zw/9b5JGvWQFV0OW3SqtDpHuXS8F1ih5+01g9cZJzqDhjiXS309Lq6fo+pA2ejBom77IGqaHs67PP8/7w6VDm6Qb/3aej0ry4VnOf8o3/CVF9D32/q4F0ocjpEbdpVvmON/76GzpwBrp+j+l5v2LX+ab/ZyBZN2WUuwWaeynUpcLpA2/Sl9fJrU5Xbrq+5LLtfF36atLpNZDpKt/VJm9Pci5D2+ZK4V3Pvb+lr+kz0dLLQdJ1/5aeL4/HpYWvSud+ZQ06M5j75tgdGIvZ1A3YYX0+4PSuu+lC99xjq/Twlk3n1/sPJ4m9nYGtncsk/54RFo7zXmsmmPrRJig6fUekq+/dM86KSjM+f7fT0nzJkpD/09qNdj5WWnSSxo/M//8P93p/LwY4V2kmHWSfKTaEc79b8p2z1opuPaxeRa9J/12v/MYH3y3NGnAsfEmmDPbHLdVdwS30bNB2/RPzDhJJxUuuzlu9q+WrpsuqYZ6xHwnvf6C+tVqowVB261J4lVTyrk4/3yzXrLqeZR8Ncq8ft2hk0OaSudc5Bxfv73SY3dqlyNclapBBzWKeUoTHbOtMiissfN4MPvXHAu1mlTu+r2A12VkPZL5dmo9HnR+a5bkl1aGjAuAot06T3o8XnrkgNS017H3TQDaZqhz3P1bnVkik8FxZUlMEDvieeeXy4MbrLda+R5Qhvnuf+cKyTfAuQwj+eCxz3G3MdK5r0qZKc5/VGkJUk6Br6SudVgZqjjnP/SHdjv/uWdnFHNuOPqYcUR+ORlSeuKxTFdJzDaZbTMZaZMRTXYup2HqNuuxgU9i4XmsaRxFZ9kKsjLQjtzz1rFlHH19dH35tqHgtIXWf9BZZyaIzTef67EUmayyTFvc/K66K27fFaXg/nIx+9XsX3NMmEy+q1yucjbrK105zTmf2XZz3FgZWpPxPChdNNl5rPa97sS2x6zTHJMmq5snu5+vHLnPi6gzM/68idKp9zqD2I6jnF9IEndL5tfDjCRn1rbgPK7luT4HKSYrfMj5PG6rNbpxmvNYrJlVzP88V50fPW5qZDinq3F4mzY6ItQ//S3V90lyfhkseGwOvkffnrNKVzf/Xbp5toIyEo6Nv/JbXdLwe61xtFGl6jRKP5y/Spc1+VW6bZHzC1Le/YtyI5AFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGzJ6/qRjYqKsobU1FR3FwUAAADl4HUZWY+8sxcAAADKzOsCWQAAAFQPBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALAAAAWyKQBQAAgC0RyAIAAMCWCGQBAABgSwSyAAAAsCV/eZmoqChrSE1NdXdRAAAAUA5el5GNjIzUlClTFBIS4u6iAAAAoBy8LpAFAABA9UAgCwAAAFsikAUAAIAtEcgCAADAlghkAQAAYEsEsgAAALAlr+tH1lM8/N1q7YhN1muX9FJ4Kefx93N+7wg8+gjvFuDnq8/n75SPj4/6tKyrAD8fHUxK10VvzVXT2iHKynHIz9dH2TmOUh0zE//erBkbY6znD4/qnPt+fEqmxn2wUI1qBevlyJ568qe1Wr/vsHXsrtiVoHdnb9PNQ9qqa9NaunfKSmVk56h1gxrWvIeOZKhT4zAt3Rmv5y7qro6Nw/KtMz0rWzd/tlQOOazlb9yfpFfG9lRE3VAt3hGn535dry7BORpRTJmnLN6trxbv0v0jOmpg2waFxj/w7UrtS0zT65f2Vr0agfpr3QEdWLhLV5SifvclpqqJczNKtGhHnJ6fPVevNE1WG6u+MlQ3z/gnflqnzQkO9Yioo4XbYjW5YZoaSpqz5ZAOB+3XqGKW+8OKvfI9uFcXStp04IgeeWe+zmyUpBuPjr/jq+V67orTNG3ZHn2/Yo/13ukdw/XvxhgNaFNfa/YkqmUtXz1Tim1NzczWjZ8usY6hSZJ8jr7/y6p9+nZptEYeZ/7Y5HTVP/p8w/7D+uH3DfqPpISUTD09ZaW1TwsydfHC7xus58M7N9Jtp7crRUmlnbHJeu/71Yo9kmEd2xMv65077s91B/TWv1v0ZESiekjKdjh0/UeLlJiaqQY1g9SsTojW7TusVyJ7qnm90OOu66eVe/XRL3P11AXd1K1ZbVWGvYmp2rblkBKD9umcAuM+nrddffcmKjpwvybPnavzU7brzLBURRSxnNf+3KR5Ww/phdE91LZhzQop2xM/rrU+kx8H5GhjdKI2bYzWGEl7ElIUlpapWnmmnfD1cj19xTDVDg0otJyktCzl/+QXtjchTa/8sdH63HdtWluPntsl3/gXf9+gDUF1dU5g/vl8jh6sl7y7QO+nZOjn+TvUcXusBkhasjNeb8ZuUbvwouvDdV4M8vfVtGXRCg7wU5uGpfjQn4CDR9J137cr9HKlLN27Eci6yXfL9yglI1tbDhwpdSB73aBW6ja4p4Z1Lu0cqM7+c3YnLdwWZz3v16quGoYF6d2r+liB1N3frLTef/uKk6wTvQkUNuxPKnF5Jmgxx5YJOudtjVXtEOc/pF1xKdZglmMCWRNgxSVnWMGsCXyX70rQvxsP5v5THn9aWz3y/Zrc5c7a5By3ZGdcoUDW/IP7fe3+fO+t3XvYCmTnbjlkLTujniusKuyPdQesaeZsPlRkIDt12R4r2Nl+6Ijq1ainmZsOqlZCmlT4f20hO+NS1KTG8YOdNXsOW2XYHpisNnWkPfGp+QJZEwzFZgZq7pZY6/W+IGcga/bTsu1xxQaypqxXN/WXUqVNMUlalBknv4T43EB21uZDVpA+fe1+dWta2wq8X/1zkzVu2a4E63G5X6aeKcW2mi8rJgg0HHWOBbJm//ZtVU9Ds8KlbcXPvzs+NTeQXbwjXlsPHpECZX2pmbosushAdu7WWIUE+Kl7RG3r2CttIGvOm58v2JX7+tCR9NznMzc5j8f1foetQDYzK0eLtsfppTE9dftXy+RwOKdbvSexVIHsgm2xWrY/RAu3x1VaIGs+W+ZYWLo9rlAga46BjqlZmr5mv1r0CFU9BSomKa3IQPbnVXu19WCytf0VFcj+sGKPdWwkt8rShgNJqhXkL6VIMUkZSkpMzRfIzt8aq93xKaodWriezHF6vEC2WZ1g3fXPFuv5lpgjhQJZc0wty0mQChwm1nkqR7phcGtlfp+j75fv0fONzMEnLdweqybNg/XMhd2kNOey8zLns7V7E9Wvdb3cc2mvFnVUGbYdTJaPT4IUVCmL92qk9mykYc0gndOjifWtEWhw9HgwQ3itYCszO7RjuM7u1iS3clo3rKGR3ZrkZvOPx5zQTRB5ouqEBPJFqwLVCy3df72ezetUWPBSUMdGYQoNrJxzTot6oerXsp4qU6C/r/UZ8XWl7myqU+NaalQ72K1lML9qVJa65Vz2qO5Ncr+A1Tn6Jdww57OmdYq+AVKL+qE6u3uTfOdSk7WHvRDIAgAAwJYIZAEAAGBLBLIAAACwJa+72CsqKsoaUlNT3V0UAAAAlIPXZWQjIyM1ZcoUhYTQoBsAAMDOvC6QBQAAQPVAIAsAAABbIpAFAACALRHIAgAAwJYIZAEAAGBLBLIAAACwJQJZAAAA2BKBLAAAAGyJQBYAAAC2RCALAAAAWyKQBQAAgC0RyAIAAMCWCGQBAABgSwSyAAAAsCUCWQAAANgSgSwAAABsiUAWAAAAtuQvLxMVFWUNqamp7i4KAAAAysHrMrKRkZGaMmWKQkJC3F0UAAAAlIPXBbIAAACoHghkAQAAYEs+DofDIS8UFBSkhg0bVtn6jhw5opo1a1bZ+uyAOqFOOE743HAu4fzK/xz3OWKD2OTgwYNKT08vdrzXBrJVLSIiQtHR0e4uhkehTqgTjhM+N5xLOL/yP8d9IqpBbELTAgAAANgSgSwAAABsiUC2itxzzz1VtSrboE6oE44TPjecSzi/8j/Hfe6pBrEJbWQBAABgS2RkAQAAYEsEsgAAALAlAtlKtnnzZg0cOFAdOnRQ//79tW7dOnmDVq1aqVOnTurVq5c1fPPNN9b7MTExGjlypNq3b69u3bppzpw5ufOkpKTosssuU7t27az6mjZtmuxswoQJVj34+PhozZo1ue+faB3k5OTojjvuUNu2ba3xb731lqpLnQwdOlRt2rTJPV5ee+01r6iTtLQ0XXjhhdZ2me02x8WOHTu8+jgpqU689TgxzjrrLPXo0cPa7lNPPVUrVqzw6uOkuPrw5mPE5cknn8x3jq32x4jpRxaV5/TTT3d89NFH1vOoqCjHySef7BXV3bJlS8fq1asLvX/ttdc6Hn/8cev5okWLHC1atHBkZmZar5988knH1VdfbT3ftm2bo1GjRo64uDiHXc2cOdOxe/fuQnVxonXwySefOM444wxHVlaWIzY21lru+vXrHdWhToYMGeL46aefipynOtdJamqq45dffnHk5ORYr9944w3HmWee6dXHSUl14q3HiREfH5/7/LvvvnP07t3bq4+T4urDm48RY+nSpY6RI0dax4HrHFvdjxEC2Up04MABR+3atXMPGHNiNgfJ9u3bHd4ayNaoUcMRExOT+7pfv36OGTNmWM+7dOlifchcIiMjc78EVKe6ONE6GDVqlGPKlCm54+6///7ck5PdlCWQ9ZY6MRYvXuxo27at9ZzjpHCdcJw4ffzxx44+ffpwnBRRH958jKSlpVnJMhOQ5j3HVvdzCU0LKtHu3bvVtGlT+fv7W69Nqr9FixbatWuXvMEVV1yh7t2764YbbrBuMRcbG2v9VJH31sDmZ2ZXfZjHli1bFjmuuihPHVT3+rn//vut4+WSSy7Rtm3bct/3pjqZOHGizjvvPI6TIurExZuPk6uuukrNmzfXI488ok8++cTrj5OC9eHtx8hjjz2mK6+8Uq1bt/aq/zkEspXMBK95ecsdgWfNmqWVK1dq2bJlql+/vq6++upS1Ufe8dW1rspTB9W1fj777DOtX79eq1atstq7nXvuuV5XJ88995zVpv7ZZ5+1XnOcFK4Tbz9OPv30UytB8swzz1jBmrcfJ0XVh7ceI/Pnz9fixYt16623FhpX3Y8RAtlKZL4pmnsYZ2Vl5R4E5kNnsrLVnWsbAwICdNddd2n27NlWQGuY7KzLzp07c6c1j66LOgqOqy7KUwfVuX7MZ8V10rz99tutLIrJJHhLnbz88svWRRa//fabQkNDOU6KqBPD248TF5MYmDFjRu5rbz+fuOrDHAveeozMnDlTGzZssLKxJnNqYo8RI0Zo0aJF1f8YcXfbhurOtNfJe7HXgAEDHNXdkSNH8jXEf+WVVxynnnqq9dw0Ks/b6Lx58+a5bYjN+3kbnYeHh1sNzO2uYHvQE60DcxwNGzYst+G9abC/bt06h93rxGz7/v37c8d9++231ra5VPc6MZ+Pk046qdCFjd58nBRVJ958nCQmJjr27NmT+3ratGmOZs2aWdddeONxUlx9ZGRkeO0xUtI5trofIwSylWzDhg1W4+v27dtbjdHXrFnjqO62bt3q6NWrl6N79+6Obt26Oc4///zcC9zMScZcgdyuXTurkfm///6bLwAeO3asdWGHqS8T+NvZrbfeap1c/fz8rIv8XBesnGgdmJOJWWabNm2swVzNXR3qxGyz+WyYY6VHjx7WVbIrVqzwijoxPTiYfIIpe8+ePa2hf//+Xn2cFFcn3nyc7Nq1y7pAx7XtJrhYvny51x4nxdWHNx8jJQWy1f0Y4Ra1AAAAsCXayAIAAMCWCGQBAABgSwSyAAAAsCUCWQAAANgSgSwAAABsiUAWAAAAtkQgCwDHYe6U06lTJ/Xq1St3WLdunW3q7eOPP9aYMWPkCTypLADsz9/dBQAAO/j222/VrVu3Slm2uY21vz+nYwAoKzKyAFAO5p7uL774ogYMGGDd5/yjjz7KHbd582adc8456tevn3r27Km33nor33yvvPKKhg4dqoceekh79uzRsGHD1LVrV5177rnW8Oabb2rv3r1q1KiRUlJScue97LLL9PbbbxcqS0ZGhu6//351797dWt/IkSNzxyUlJVnzmXF9+/a17kFv7N+/X6effrr69OljrXvChAnmjo/WuCeeeEKXX365zjvvPHXp0kVnnHGG4uLicjOr5l7uRS3T+Oyzz6w6OemkkzRkyBCtWbOG4wxAhSMFAAClYH4ODw4Ozn29aNEiBQYGWs/N+wsXLtT69evVv39/jRs3zgpUTRBoAjrTLMEEoieffLI1mODOSE9P17///ms9Hz16tBVQPvLII9q1a5eV/TWBaNOmTTV8+HB9+eWXuuGGG6zA86+//tJ7771XqIzPP/+8tm7dqiVLligoKEgHDx7MHWfKt3LlSrVs2VIPPvigFXxPnjxZderU0U8//aSaNWsqOztbF1xwgaZOnZr787+Zb/HixapXr54uvfRSax4TeJe0zLlz5+rrr7/WrFmzrHLMnj1bV1xxhTUtAFQkAlkAKGfTAhOkGZ07d7aaCJhg8/Dhw1q7dq0V/OXNipq2ta5A9rrrrssdN2PGDE2cONF63qJFCys763LnnXdq/PjxViBrAkUTIJvAs6Cff/7ZyvKa4NFo2LBh7rjBgwdbAadxyimn6I033rCe5+Tk6D//+Y/mzJljZWJjYmKsNsCuQPbss8+2gljXfKtXrz7uMn/44QcraDUZWRcTVJuMMQBUJAJZACinvJlaPz8/q82rCQobNGigFStWFDtfwWDUZHGLYrK8Zh0zZ860MrH//PNPhZTRePXVVxUbG2tlV80099xzj9LS0o47X0njzLabIP2pp54qczkBoCxoIwsAlaBjx44KDQ3Vp59+mvveli1bctuYFmTaypp2p8bu3bsLBasmK3vllVda7Vg7dOhQ5DLOP/98/e9//7OaLBh5mxYUJz4+Xo0bN7aC0gMHDigqKkrlZdrUmu022+HK+prmDgBQ0QhkAaAUzE/tebvfMu0+S2KaGJi2p1OmTFGPHj2sANQ0DUhNTS1y+tdff11//vmndZGWyYoOGjRItWvXzrf+I0eO6Pbbby92naaJQNu2bdW7d2+rjFdfffVxt8tc3DVv3jxrepNFNe1xy+u0007Tc889Z7W3NdtjmmR888035V4uABTk43BdngoAcBsT4AYEBFgB8L59+6yeDv7++28rs+u6uMxkZDds2CBfX3IQAGDQRhYAPIDpquuqq66y2pdmZmbq8ccfzw1iTSb3jz/+0Pvvv08QCwB5kJEFAACALfH7FAAAAGyJQBYAAAC2RCALAAAAWyKQBQAAgC0RyAIAAMCWCGQBAAAgO/p/pnQYcyy+yDEAAAAASUVORK5CYII=", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for idx, (spct1, spct2) in enumerate(zip(summed_spectra, single_ch_spectra)):\n", - " plt.figure(figsize=(10,10), dpi=80)\n", - " plt.hist(spct2, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(spct2), log=True, histtype='step', label='SiPM-Ch.1-4')\n", - " plt.hist(spct1, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(spct1), log=True, histtype='step', label='SiPM-Ch.5-8')\n", - " plt.legend()\n", - " plt.xlabel(r'Energy channel')\n", - " plt.ylabel(r'Count rate ($s^{-1}$)')\n", - " plt.title('Current comparator background spectra, SIPHRA-Channel '+legend[idx])\n", - " plt.xticks(np.arange(0,4500,500))\n", - " plt.grid()\n", - " plt.show()" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "# Charge Comparator background spectra\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Parameter
Value
Inactive channels
Active channel
ccmis_detector_voffset
127
cmis_detector_ioffset
7
cc_threshold
1
" - ], - "id": "526643d5d320fa61" - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/260205.ipynb b/noteboks/260205.ipynb deleted file mode 100644 index 20836a1..0000000 --- a/noteboks/260205.ipynb +++ /dev/null @@ -1,288 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:26:51.313673500Z", - "start_time": "2026-02-06T13:26:51.261224100Z" - } - }, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4b55b402-ac25-4996-9c5b-763602446cbb", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:26:55.213845600Z", - "start_time": "2026-02-06T13:26:51.321486100Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": " Unnamed: 0 Detector ID Trigger Time_sub Time_sec Time_gps \\\n0 0 5.0 401139.0 21.0 2310347.0 9687.0 42069.0 \n1 1 5.0 401140.0 20.0 2310347.0 9687.0 42069.0 \n2 2 5.0 401141.0 21.0 3799888.0 9687.0 42069.0 \n3 3 5.0 401142.0 20.0 3799888.0 9687.0 42069.0 \n4 4 5.0 401143.0 21.0 3949203.0 9687.0 42069.0 \n... ... ... ... ... ... ... ... \n99831 99831 5.0 501130.0 21.0 8088842.0 9856.0 42069.0 \n99832 99832 5.0 501131.0 21.0 8230393.0 9856.0 42069.0 \n99833 99833 5.0 501132.0 20.0 8230393.0 9856.0 42069.0 \n99834 99834 5.0 501133.0 21.0 8416551.0 9856.0 42069.0 \n99835 99835 5.0 501134.0 21.0 8457518.0 9856.0 42069.0 \n\n Temp Ch1 Ch2 ... Ch9 Ch10 Ch11 Ch12 Ch13 \\\n0 149.432817 150.0 230.0 ... 129.0 140.0 123.0 117.0 120.0 \n1 149.432817 149.0 266.0 ... 129.0 138.0 122.0 116.0 120.0 \n2 149.432817 150.0 211.0 ... 127.0 140.0 123.0 116.0 119.0 \n3 149.432817 150.0 626.0 ... 130.0 141.0 123.0 118.0 121.0 \n4 149.432817 150.0 225.0 ... 127.0 140.0 122.0 117.0 119.0 \n... ... ... ... ... ... ... ... ... ... \n99831 149.432817 150.0 474.0 ... 129.0 139.0 123.0 117.0 120.0 \n99832 149.432817 152.0 559.0 ... 129.0 140.0 123.0 118.0 120.0 \n99833 149.432817 150.0 862.0 ... 129.0 141.0 124.0 118.0 121.0 \n99834 149.432817 151.0 492.0 ... 129.0 141.0 124.0 118.0 120.0 \n99835 149.432817 149.0 300.0 ... 129.0 139.0 124.0 116.0 120.0 \n\n Ch14 Ch15 Ch16 Argmax Summed \n0 118.0 108.0 135.0 2.0 2086.0 \n1 118.0 106.0 135.0 2.0 2113.0 \n2 118.0 108.0 135.0 2.0 2062.0 \n3 119.0 108.0 135.0 2.0 2487.0 \n4 119.0 107.0 135.0 2.0 2072.0 \n... ... ... ... ... ... \n99831 119.0 109.0 136.0 2.0 2331.0 \n99832 119.0 107.0 135.0 2.0 2423.0 \n99833 119.0 109.0 136.0 2.0 2727.0 \n99834 119.0 108.0 136.0 2.0 2354.0 \n99835 119.0 108.0 135.0 2.0 2157.0 \n\n[99836 rows x 26 columns]", - "text/html": "
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Unnamed: 0DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
005.0401139.021.02310347.09687.042069.0149.432817150.0230.0...129.0140.0123.0117.0120.0118.0108.0135.02.02086.0
115.0401140.020.02310347.09687.042069.0149.432817149.0266.0...129.0138.0122.0116.0120.0118.0106.0135.02.02113.0
225.0401141.021.03799888.09687.042069.0149.432817150.0211.0...127.0140.0123.0116.0119.0118.0108.0135.02.02062.0
335.0401142.020.03799888.09687.042069.0149.432817150.0626.0...130.0141.0123.0118.0121.0119.0108.0135.02.02487.0
445.0401143.021.03949203.09687.042069.0149.432817150.0225.0...127.0140.0122.0117.0119.0119.0107.0135.02.02072.0
..................................................................
99831998315.0501130.021.08088842.09856.042069.0149.432817150.0474.0...129.0139.0123.0117.0120.0119.0109.0136.02.02331.0
99832998325.0501131.021.08230393.09856.042069.0149.432817152.0559.0...129.0140.0123.0118.0120.0119.0107.0135.02.02423.0
99833998335.0501132.020.08230393.09856.042069.0149.432817150.0862.0...129.0141.0124.0118.0121.0119.0109.0136.02.02727.0
99834998345.0501133.021.08416551.09856.042069.0149.432817151.0492.0...129.0141.0124.0118.0120.0119.0108.0136.02.02354.0
99835998355.0501134.021.08457518.09856.042069.0149.432817149.0300.0...129.0139.0124.0116.0120.0119.0108.0135.02.02157.0
\n

99836 rows × 26 columns

\n
" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "dfs = []\n", - "dfs.append(pd.read_csv('../data/260205/5_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260205/6_SiPM_ChannelsTest_Ch5-8_Ch6_QT_Thr20_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260205/7_SiPM_ChannelsTest_Ch5-8_Ch10_QT_Thr20_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260205/8_SiPM_ChannelsTest_Ch5-8_Ch14_QT_Thr20_Background.csv'))\n", - "\n", - "dfs[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cb4c1aad-40db-456b-a316-8ce713563841", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:26:57.518543800Z", - "start_time": "2026-02-06T13:26:55.204462Z" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "N_BINS = 512\n", - "BITS_12 = 2**12\n", - "\n", - "summed_spectra = [df[ch].tolist() for df, ch in zip(dfs, ['Ch2', 'Ch6', 'Ch10', 'Ch14'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a8cf1914-e98b-44cc-b77e-46ec5b15245f", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:26:58.797329900Z", - "start_time": "2026-02-06T13:26:57.507242Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [162.847, 180.959, 169.660, 136.229]\n", - "plt.hist(dfs[0]['Ch16'], N_BINS, range=(0,BITS_12), log=True, histtype='step')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "436876e4-6c49-4cc6-9552-6551a7059e27", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:27:01.546487700Z", - "start_time": "2026-02-06T13:26:58.802252300Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [162.847, 180.959, 169.660, 136.229]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Charge comparator background spectra, SiPM-Channels 5-8')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e68ab9ee-bdeb-4c34-b3ef-e032a4b3a6ec", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:27:03.307130800Z", - "start_time": "2026-02-06T13:27:01.547464200Z" - } - }, - "outputs": [], - "source": [ - "dfs_ch1_4 = []\n", - "dfs_ch1_4.append(pd.read_csv('../data/260205/9_SiPM_ChannelsTest_Ch1-4_Ch14_QT_Thr20_Background_retry.csv'))\n", - "dfs_ch1_4.append(pd.read_csv('../data/260205/10_SiPM_ChannelsTest_Ch1-4_Ch10_QT_Thr20_Background_retry.csv'))\n", - "dfs_ch1_4.append(pd.read_csv('../data/260205/11_SiPM_ChannelsTest_Ch1-4_Ch6_QT_Thr20_Background_retry.csv'))\n", - "dfs_ch1_4.append(pd.read_csv('../data/260205/12_SiPM_ChannelsTest_Ch1-4_Ch2_QT_Thr20_Background_retry.csv'))\n", - "\n", - "#dfs_ch1_4 = dfs_ch1_4[::-1]\n", - "single_ch_spectra = [df[ch].tolist() for df, ch in zip(dfs_ch1_4, ['Ch14', 'Ch10', 'Ch6', 'Ch2'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "97eb2049-7aab-4804-a96b-555d5d4c84f9", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:27:05.647528100Z", - "start_time": "2026-02-06T13:27:03.309083800Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "times_ch1_4 = [47.626, 86.144, 116.448, 72.604]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.14', 'Ch.10', 'Ch.6', 'Ch.2']\n", - "for idx,s in enumerate(single_ch_spectra):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Charge comparator background spectra, SiPM-Channels 1-4')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "aee0452b-0a8b-4f2f-83e7-94bed99372dd", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T13:27:11.969887700Z", - "start_time": "2026-02-06T13:27:05.634146Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" 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" 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56623uPbaa8ve0zfccANfffVV2fc1ODi47OPxxx+ndevWWK3WGv/OrOrnBuDTTz9l2LBhtG3bltDQUK666ioyMjKw2+2Vbl/ZPio7Bhz9Xblt2zb+/ve/l/tZ+eijj9i/f3+N6m/btm2dz/XYOo4XGhoKwKOPPkrr1q0JCwvjqaeeYv369WzdurVGxxPXU6CVZqN79+7Ex8fz/vvvV9suJCSE/Pz8cssq+4Xo5VXxx/v4Ze3atWPEiBHl/iHKzs6mqKjohDeKVHec4x0JPFX9coyJiWHHjh3llu3YsYPY2Nga1VCd3bt3l33tcDhISkoqm4EgISGBrl27sn79enJycsqCtzGm3D6OPceSkhImTpzIhAkT2Lp1Kzk5Ofz888/ltqvsmsTExJCUlFRuSp4j53zsedbkeh5/XkdeHzmvG264AYfDwYoVK8jJySEzM5PQ0NCy+jp16nTCm3quu+467rrrLkaOHMm6devKlnfs2JGMjIxy/zju2bOnwvbHn0dNzv/4n+2a/kN/xJHzP/baHH+djme1WvnHP/7Bb7/9RnJyMr169eKCCy4o1+bYfRy5qSk6Oprw8HD8/f35+uuvy72H8vPzeeWVVwDnta4uFBz7B+axjr9+//vf/3juued47733SEtLIysri+nTp1f4Wa2NuLg47r//frKzsyu8/xpTREQE77zzDl988UXZjAmJiYmkpqby0ksv0a5dO9q1a8eMGTOw2+3Mnj0bcF77Ix/33nsvAQEBjBkzhg8++KBe9ezbt4+EhARuvfVWkpKSyMnJKfs9XJ/re7x27drx8ssvl/tZycvL49tvv63R9ueeey4rVqxgy5YtDVYTQP/+/YGqfxbFPSjQSrMye/ZsEhMTmTFjBnv27MEYQ05ODu+99x733XcfAAMHDmTdunUsXboUu91OYmIiixcvrtPxrr32WtasWcPLL79MQUEBxhj27t3L559/XuN9tGvXjrS0NNLT06tsExkZyWWXXcZf//rXsl/GBw4cKJuK7Prrr+ett95i0aJF2O12fvrpJ958802mTZtWp/M61vPPP8+mTZsoKSnhscceo6SkhPPPPx+A7OxsQkNDCQsLIyMjg7///e8n3F9JSQmFhYW0atWKkJAQ9u/fX2HO1squybnnnou3tzf33nsvhYWFHDx4kL/97W+cd955tGvXrtbn9d5777Fs2TJsNhvvvPMOa9as4Yorrig7r+DgYFq1akV+fj7//Oc/ycvLK9v26quvZt++fTzwwAPk5uZit9tZuXIlaWlp5Y7xwAMP8OCDD3LGGWfwyy+/ADBkyBC6du3KXXfdRUFBAfv37+fxxx8/Yb01Of+BAwfy0UcfkZWVRU5ODvfcc0+trknHjh0ZO3Ysd911F5mZmWRmZnLvvfdWu81PP/3EypUrKSkpKZtW6thef3D2XO3bt4+CggL+/ve/061bN4YNG4afnx833ngjd911F5s2bcIYQ2FhIYsXLy4Lsbfffjtvvvkmc+fOpaSkhKKiIn788ceyfbdr165GASU7Oxtvb2/atm2LxWJh4cKFFYLcO++8U20oeeutt/j444/LenVTU1OZNWsWkZGR9OrV64Q1NKTIyEjuuOMO7rnnHux2Oy+//DLnnHMOmzZtYu3ataxdu5Z169YxY8YM3njjjUpnagF47rnnWLduHVdeeSVbt27FbrdTWFjIZ599xo033lijWvLy8nA4HERERODv78+2bduYOXNmQ54uAHfccQf//ve/WbFiBQ6Hg+LiYlasWFHhfwSqcumllzJu3DjOO+885s2bR35+PsYY1q1bx1VXXVXpH5Y1MXz4cPr378+DDz5IdnY2eXl53HPPPZxyyinEx8fXaZ/S9BRopVkZPXp0WU/R4MGDCQkJoW/fvnz//fdMnjwZgFGjRnHvvfcyadIkIiMjWbRoERdffHGdjhcbG8uyZcuYP38+Xbt2JTw8nPHjx5frkTuRMWPGcMEFFxAfH094eDgffvhhpe1ef/11Ro0axdlnn01wcDDDhw9nw4YNgLOn9JlnnuHmm28mPDycW2+9leeff55JkybV6byOddNNN3HVVVfRunVrvvzyS7799lvCw8MB5z/wiYmJhISEMGTIEM4+++wT7i84OJg33niDRx99lODgYM4++2wSEhLKtansmoSGhjJ//nz++OMPoqOjGTBgAN26dePdd9+t03ndeOON3HfffYSHh/PUU0/x2WeflQ3beOGFF/jjjz9o1aoVvXv3pmPHjuXmxY2KimLx4sWsWrWKzp0706ZNG2699VaKiooqPc4rr7zCxIkT+fbbb/H29uarr75i06ZNREVFMXbsWC6//HKAckMTjleT83/00UcJDQ0lJiaGAQMGcNFFF9X6unzwwQf4+vrSqVMn+vfvzyWXXFJt+0OHDjF16lRat25NZGQkP//8M5988km5Ntdddx1nnnkmUVFRbN26la+++qps6MTTTz/NZZddVjbsoFOnTsycObMsgJ111ll89NFHPPHEE0RGRhIdHV3W4wgwc+ZMnnzyScLDw5k4cWKVdU6dOpWxY8dy8sknExERwauvvsqVV15Zrs2ePXvKhr9UpnXr1rz22mucdNJJBAUF0bdvX7KysliwYAEBAQHVXqfG8Le//Y2UlBRefPFFli1bxr333lvWO3vk48477yQzM7PSOWsBevfuzerVq/Hz82PMmDGEhoYSHx/PO++8w2WXXVajOnr27MnMmTO5+uqrCQkJ4ZprrqlwbRvC7bffzsMPP8yNN95I69at6dixI3feeWeF/3GripeXF19++SXTp0/n7rvvJjIyksjISK699loGDBhQbkhWdZYsWUJwcHDZUB+LxcJXX32F3W4nJiaGLl26UFBQwJdfflnhjztpviymIf8/QUSaFYvFwvz58xk3bpyrS2nRPv/8cy699FIKCwtb1H9b7t69m86dO7Nt27ay8b7N2YgRI5g1axaDBw92dSki0sS8T9xERESOtWzZMtq0aUP37t3ZunUrDz74IJdffnmLCrPuaOnSpa4uQURcREMORERq6cCBA5x11lkEBQUxduxYhgwZwrPPPuvqskREPJaGHIiIiIiIW1MPrYiIiIi4NQVaEREREXFruikM8PPzIzIyssmOV1xcjJ+fX5Mdzx3omlSka1KerkdFuiYV6ZpUpGtSka5Jee5yPVJTUykuLq50nQItzgmuG+pxqzUxb948xo8f32THcwe6JhXpmpSn61GRrklFuiYV6ZpUpGtSnrtcj2PnEz+ehhyIiIiIiFtToBURERERt+bRQw4SExNJTEyksLDQ1aWIiIhIE3A4HADY7XYXV9K8NIfrYbFY8PKqW1+rRwfahIQEEhISqh2TISIiIu7P4XCwZ88eioqKiIyMZOvWra4uqdloTtfD39+fuLi4Wgdbjw60IiIi4hkOHTqEl5cX3bt3Jzc3l9DQUFeX1Gzk5OQ0i+thjCE5OZlDhw7Rrl27Wm2rQCsiIiItmjGGrKwsOnXqhLe3N15eXlitVleX1Ww0p+sRFRXF7t27iYqKwmKx1Hg73RQmIiIiLZoxBmMMPj4+ri5FTsDHx6fs+1Ub6qEVERGRFq2ycFRUaqfU7miU4/lYvfD3aR49nu5KgVZERESkGsU2B+P+byGpuZU/daq+IkP8WHLXGQq1TUiBVkRERDxKqd1Bam4xy/45hmC/ho1CecU2hs78iVK744SB9tNPP+Wxxx7DbrdTVFREhw4dWLBgARMnTuTZZ5+lR48eTJ06lfnz5xMZGUlRURGjRo3ixRdfxMfHh06dOlFQUEBycnLZcIqFCxcyZswYbr/9dp577rlKj7tjxw7uvvtuVq1aRevWrQG46aabuP7665k6dSr9+vXjjjvuOOG5Pv7447z77rts27aNTz/9lAsvvPCE2/z000+ceeaZPPPMMzU6Rk0p0IqIiIhHCvbzJsTfNeNqDxw4wLRp01i1ahVxcXEArF69GovFwrfffluu7Z133skdd9xRFmhfffVVbr31VgBiY2P58ssvufjiiwF48803GThwYJXHPXjwICNGjOCRRx7hk08+AWDPnj189913tT6HcePGcemll/KXv/ylRu2zs7O55557OOecc2p9rBPRTWEiIiIiTSwlJQWr1VrWQwrQv39/LBYLnTp1Yu3atRW28ff3Z9SoUWzZsqVs2bXXXstbb70FOAPj8uXLmTBhQpXHfemllxg5ciQ33HBD2bLw8HBuvPHGstebNm1i7NixxMfHM2nSJEpKSird1+DBg+nSpUuNz/mWW27h/vvvp02bNjXepqY8OtAmJiYyZcoUPSlMREREmlTfvn0ZMWIEcXFxXHTRRTz11FMkJydXu01mZibff/89AwYMKFs2fPhwdu/ezf79+/noo49ISEiodgquVatWMXTo0GqPs3btWr766is2bdpESkoKc+fOrd3JVeKTTz7By8uL888/v977qoxHB9qEhATmzJlDQECAq0sRERERD+Ll5cXcuXP59ddfmTBhAr/88gt9+vRh+/btFdo+9dRT9OvXj7FjxzJ58mSmTp1abv1VV13FO++8w1tvvVXj//6vzkUXXURgYCBWq5XBgwezY8eOeu3v4MGDPProozz//PP1rq0qGkMrIiIi4iI9e/akZ8+eTJ8+nQkTJvDll19WaHNkDG1Vrr76avr37098fDzdu3cvt27y5MllIfnHH39kwIABLFu2jL/97W9V7s/f37/sa6vVis1mq9U5LViwgH/84x+As/OwX79+HDhwgH79+gGQlpbGl19+SWpqKo899lit9l0VBVoRERHxSHnFtQtqDbnP5ORkdu/ezfDhwwHncIJdu3bRtWvXWh+zQ4cOzJw5k549e1ZYd+TGryNuvvlm+vXrx9tvv821114LQFZWFv/73/+YPn16rY9dmXHjxlUYA5ySklL2dW1mUqgpBVoRERHxKD5WLyJD/Bg686dG2X9kiB8+1upHddpsNh555BF27dpFYGAgNpuNa665hgsuuIDbb7+91sc8Ek5PpH379ixdupR77rmHRx55hJCQELy8vLjttttOuO3KlSt58MEHy2ZhePTRR3n11VdJTU1l/fr13HLLLaxZs4bIyMha119fCrQiIiLiUfy8vVhy1xkufVJYXFwc8+bNq3Td7t27y75+5513qtzHse2O9fDDD1d77O7du5e70Ss7O5uwsLBKj/f000+XfT1w4MByU4rdf//93H///dUeqzLVnVNdKdCKiIiIx/H3sepJXi2IR89yICIiIiLuT4FWRERERNyaAq2IiIiIuDUFWhERERFxax59U1hiYiKJiYl69K2IiIinKS0Ce0nj7NvqCz7+J24nDcajA21CQgIJCQlER0e7uhQRERFpKrYieH4Q5KWcuG1dBEfB7X+eMNR++umnPPbYY9jtdoqKiujQoQMLFixg4sSJPPvss/To0YOpU6cyf/58IiMjKSoqYtSoUbz44ov4+PjQqVMnCgoKSE5OxsfHB4CFCxcyZswYbr/9dp577rlKj7tjxw7uvvtuVq1aRevWrQG46aabuP7662v10IPRo0ezZ8+esim/rrnmmiqfQPb7779z2223UVxcTFFREddeey133XXXCY9RUx4daFuqrIISbvvfWiKD/XhmyimuLkdERKR5sZc6w+zfNoJfSMPuuzgXnu3t7P2tJtAeOHCAadOmsWrVKuLi4gBYvXo1Foul3FyvcPTRt0cC7auvvsqtt94KQGxsLF9++SUXX3wxAG+++SYDBw6s8rgHDx5kxIgRPPLII2VPEduzZw/fffddnU732Wef5cILLzxhu2nTpvHII49w/vnnk5GRQc+ePZk4cSK9e/eu03GPpzG0LYwxhh82prB4aypzV+9zdTkiIiLNl18I+Ic27EcNA3JKSgpWq7WshxSgf//+WCwWOnXqVOHRsQD+/v6MGjWKLVu2lC279tpreeuttwDnAxKWL1/OhAkTqjzuSy+9xMiRI7nhhhvKloWHh3PjjTeWvd60aRNjx44lPj6eSZMmUVJS/6EZFouFrKwsAPLz8/H19S137vWlQNvCLN2exv2frWdEtwhXlyIiIiJV6Nu3LyNGjCAuLo6LLrqIp556iuTk5Gq3yczM5Pvvv2fAgAFly4YPH87u3bvZv38/H330EQkJCVitVT8wYtWqVQwdOrTa46xdu5avvvqKTZs2kZKSUu6pYse75557OPnkk7nkkkvYuXNnle3efvttHnjgAWJjY4mPj+fxxx+nXbt21dZRGwq0LUxBiZ34dsE8ldDX1aWIiIhIFby8vJg7dy6//vorEyZM4JdffqFPnz5s3769QtunnnqKfv36MXbsWCZPnszUqVPLrb/qqqt45513eOutt/jLX/5S79ouuugiAgMDsVqtDB48mB07dlTa7v3332fz5s38+eefjBw5kokTJ1a5zyeeeIKZM2eSlJTEhg0buO+++9i4cWO9az1CgbaFe2PJThwO4+oyREREpBI9e/Zk+vTpfP755wwZMoQvv/yyQps777yTtWvXsnr1ah588EEsFku59VdffTUvvPAC/v7+dO/evdy6yZMn069fP/r160d6ejoDBgxg2bJl1dbk73907K/VasVms1XaLiYmBnAOJ7jlllvYuXMn6enpLFiwoOyYjz32GGlpaXz22WdcfvnlAHTp0oUhQ4bwyy+/nPgC1ZBuCmuhokL8uXN8Dx79ZhPnndKBqFBNHyIiIlJOca7L9pmcnMzu3bsZPnw44BxOsGvXLrp27VrrQ3bo0IGZM2fSs2fPCuuO3Ph1xM0330y/fv14++23ufbaawHIysrif//7H9OnT6/xMW02G+np6URFRQEwd+5coqKiaNOmDePGjSs3BthutxMUFMRPP/3EmDFjSEtL47fffmPGjBm1PteqKNC2UF5eFm4a1ZWn5m05cWMRERFPYvVxTq31bMPcYV9BcJRzLtpq2Gw2HnnkEXbt2kVgYCA2m41rrrmGCy64gNtvv73WhzwSTk+kffv2LF26lHvuuYdHHnmEkJAQvLy8uO2220647cqVK3nwwQf59ttvKS4u5txzz6W4uBgvLy8iIiIq7V0GZy/vnDlzuPPOO7HZbJSWlnLHHXeccCxvbSjQioiIiGfx9nfOE+vCByvExcUxb968Stft3r277Ot33nmnyn0c2+5YDz/8cLXH7t69e7kbvbKzs8vmkj3+eE8//XTZ1wMHDiybUiwoKIiVK1dWe5xjjRs3jlWrVtW4fW0p0IqIiIjn8fHX07xaEN0U1oKU2Bys3pPp6jJEREREmpQCbQsyd/U+Pvo9iXNP7uDqUkRERESajAJtC2KzOxjUqTU3ja79HZIiIiIt1ZFprozRNJbN3ZHv0fFTk52IR4+hTUxMJDExkcLCQleXIiIiIo3Ey8sLHx8f0tPTadOmDQ6HA7vd7uqymo3mcj2MMaSnp+Pj44OXV+36XD060CYkJJCQkEB0dLSrSxEREZFGFBsbS1JSEhkZGRQWFhIQEODqkpqN5nQ9fHx8iI2NrfV2Hh1oRURExDP4+vrSrVs3HA4H8+fPZ9y4ca4uqdlYsGBBs7geFoul1j2zRyjQioiIiMc4EpisVquLK2le3P166KYwEREREXFrCrQiIiIi4tYUaD1A4sq92B2aqkRERERaJgXaFsxigTvH9+CFn7azYX+2q8sRERERaRQKtC2YxWLhr2d0I9Rf9/6JiIhIy6VAKyIiIiJuTYFWRERERNyaAq2IiIiIuDUFWhERERFxawq0IiIiIuLWFGhbiO2HcvlsTbKryxARERFpcgq0LcTna/ZTYndwx7h4V5ciIiIi0qQUaFuQkzuGc3J0mKvLEBEREWlSCrQiIiIi4tY8+hFSiYmJJCYmUlhY6OpSRERERKSOPLqHNiEhgTlz5hAQEODqUkRERESkjjw60IqIiIiI+1OgFRERERG3pkArIiIiIm5NgVZERERE3JoCrYd4Y8kusgtLXV2GiIiISINToPUAj154Mr/uSOP3XRmuLkVERESkwSnQeoAJJ7WjbYi/q8sQERERaRQKtCIiIiLi1hRoRURERMStKdCKiIiIiFtToBURERERt6ZA60EKSmyuLkFERESkwSnQeojY1oHc/r+1LNx8yNWliIiIiDQoBVoP8dIV/Tk9PpL92YWuLkVERESkQSnQegirlwWrxdVViIiIiDQ8BVoRERERcWsKtCIiIiLi1hRoRURERMStKdCKiIiIiFtToBURERERt6ZAKyIiIiJuTYFWRERERNyaAq2IiIiIuDVvVxfgSomJiSQmJlJYqKdniYiIiLgrj+6hTUhIYM6cOQQEBLi6FBERERGpI48OtCIiIiLi/hRoW4BtKbks35nu6jJEREREXEKBtgV4fclOim0OLhscc8K2+cU2jDFNUJWIiIhI01CgbSHG9mpL3+jwatvEtQni8W83878Ve5umKBEREZEm4NGzHHiaByf2Jr/YxoHsIleXIiIiItJg1EPrQby8LFi9LK4uQ0RERKRBKdCKiIiIiFtToBURERERt6ZAKyIiIiJuTYFWRERERNyaAq2IiIiIuDUFWhERERFxawq0IiIiIuLWFGhFRERExK0p0IqIiIiIW1OgFRERERG3pkDrgewOB8YYV5chIiIi0iAUaD1MuzB/Xlq4g1d+3uHqUkREREQahLerC5CmdduY7uQX20hKL3B1KSIiIiINQj20HsbLy0KAr/6OERERkZZDgVZERERE3JoCrYiIiIi4NQVaEREREXFrCrQiIiIi4tYUaEVERETErSnQeqidqflc89bvvP3LLleXIiIiIlIvmr/JQ/2+OwOA1Nxirh3e2cXViIiIiNSdemhFRERExK2ph7alKi2EOdeAxQIJ70JhJqRvg7gRnBIdRrtQf9qF+VNic7i6UhEREZF6UQ9tS1WQAdvmwdbvnWE28Rp49zxY+wFje0Wx/N6x/PWMbuW3eXUEPB0P2cmuqVlERESkDtRD6wl++jcUpENIe/jyVrBYoVUcZ3w6BeM1FBjpbHdwnfNz7kEI6+iyckVERERqQ4G2pZvyHnx2E5Tmw5T3YfM3kLUHbEV42/Lpat1Z9bYOh7N3N6AVeKkzX0RERJonpZSWrvcF4Bvo/NrLG7x9a77tkmfgqS7w85ONU5uIiIhIA1APrSe48BVI3QzdxsLW78qtCjT57Js9GYLbEn38djn7yn8WERERaYYUaD1B9zOdH5Vo70iBA/PLLSuddz8+CW81RWUiIiIi9aYhB1LOjSV3YDJ2w++vgcPm6nJERERETkg9tJ7G6gtrPzo6rvY4ax1dSe95Oe1XvAYl+U1cnIiIiEjttbhAm5GRwZlnnsmWLVvIy8tzdTnNz+h/QsxpUJLH3oMpxKx8okKTlH630j7Uj5JfX2G/PRzvzMKK42tFREREmokWF2hDQkKYP38+U6ZMcXUpzVNQBPR1XpuYwixySw5hwuPIXTobMOAfytPztjDrktt4Nv1s2q56luFZCrQiIiLSfLW4QOvj40Pr1q1dXYZ7CAgnZNKzAISOuQOA/+zO4LaP1rAmKct1dYmIiIjUQrO+Key2226jU6dOWCwW1q5dW27dtm3bGDZsGPHx8QwaNIgNGza4psgWZlCn1rQJ9sXuMBSX2gEYmPUdPBwGide6uDoRERGRiizGGOPqIqqyePFiunTpwogRI/j888/p169f2boxY8Zw9dVXM3XqVD755BOefPJJVqxYUbZ+3LhxLFiwoNL9zpo1i1mzZpW9zsrKYu7cuY12HscrKirC39+/wfb33mY7rfwsnNf56N8nfsXpjF59E/OGzqn1/h5bYSO7BLJLYLj/Lu70TqRDsBcBJen8espTDVb3sRr6mrQEuibl6XpUpGtSka5JRbomFemalOcu1+O6665j374q5sY3biAuLs6sWbOm7HVKSooJCQkxpaWlxhhjHA6HiYqKMtu2bStrM3bs2Brvv2PHjg1Wa018//33Dbq/OxPXmmfnbym/MGufMQ+F1ml/576w2MTd/bV5c8lO8+z8LebOxLXGbP7OmJeHGZOXZkxhVgNUXV5DX5OWQNekPF2PinRNKtI1qUjXpCJdk/Lc5XpUl9ea9ZCDquzdu5f27dvj7e0cAmyxWIiNjSUpKQlw9s6uWbOGcePGsX79eleW6tZ8rJajL/yCIWW981G4/xkADrvrChMRERE5Rou7KQyocqiB1IyFw0HWckyg7TQCpi0CL294bTS8MxGu/AR8g1xRooiIiEgZt+yhjYmJ4cCBA9hszidZGWNISkoiNjbWxZW1DI9ddBIPn9ebi/t3LL+iw6nQ7mSYvhiSfoWCDNcUKCIiInIMtwy0bdu2pX///nzwwQcAzJ07l+joaLp16+biylqGvtHhTB3emUBfZwf+gk2HOPv5Jbzw4zZng6g+gKXqHYiIiIg0oWY95GD69Ol88803HDx4kPHjxxMSEsL27dsBmD17NlOnTuXxxx8nNDSUt99+28XVNjMOW4Ps5rxTOpCcWUhaXjFf/rGfghI7r/68g53+kF9sI6RBjiIiIiJSd8060M6ePbvKdT169GDZsmX12n9iYiKJiYkUFhbWaz9NyeEw/D3xDzLyS3jh0lMJC/Sp2ChzD7w8FEI61Pt4XSODeSrhFH7clMJ1765kb0YBfz8zHhYr0IqIiEjz4JZDDhpKQkICc+bMISAgwNWl1JjdGD5bk8zPW1PZm1lQeaP8NPANhFtXNthxwwKcwbljeAC3ju0OgMV2+A+BhY/D4x1h3ScNdjwRERGRmmrWPbRSDxZrg85AMLBTa378+yjCDwfbvUQS995ICGoLfiFQkgcpG+DkyQ12TBEREZGa8OgeWqmdrpHBtAn2A+DMkqc5eO1vENAKMna4uDIRERHxZAq0UieleGMPi9M8tCIiIuJyCrQiIiIi4tYUaKXOftp8COPqIkRERMTjeXSgTUxMZMqUKW41bVdzMXVYJ/791UYWpwUDUBLc8QRbiIiIiDQOjw607jhtV3Px0Hl9uGpoHDP9/85pXh+xK3wYHPgDsve5ujQRERHxMB4daKV+HpjYm+9njGZgt3a8cLA3BYd2wKKZri5LREREPIwCrdTbv87vQ16HEawKOwuMRtWKiIhI01KglXqLCPYjupWGbYiIiIhrKNC6uZScIvZlNqOb2vathKfj4cvbXF2JiIiIeAgFWjd3/+fr2ZNewJAubVxdilPaFshLgX0rXF2JiIiIeAgFWjdXYnNwy5huzSfQioiIiDQxBVppMHYvX+cXXt6uLUREREQ8ikcHWj1YoeFEhvgxfetAvu/zNJw7C+ylUFLg6rJERETEA3h0oNWDFRrOrWO6c/XIHnxdOoAv0ztgz9yD/fWxri5LREREPIBHB1ppOFYvCyH+Pnz95wFu+6mECwofJD9tr6vLEhEREQ+gQCsNbmT3CJ6/bAAOPWRBREREmoDu3pEGM3lANPklNsb1ioKcza4uR0RERDyEemilwXQID+CfZ/diUKfWAPhSCvtWubgqERERaekUaKVRlIREs404eGMMfP9PyN4HB/5wdVkiIiLSAinQSqNw+IVxjeVRuOBlzPJXKH5uAMw+Hbb/6OrSREREpIVRoHVjHRf9jVf3XkBkRjP9b32LBU69ghJjxc8UOZcV57q2JhEREWlxPPqmsMTERBITE932wQr+6RsJMAWE5u1ydSlVstkdXFt6NxFkM7PDEoJcXZCIiIi0OB7dQ6sHKzSugmI7Q2b+xDqfU/jBOgJjsbi6JBEREWmBPDrQSuMqsTtIyyvm57vOoHWgr6vLERERkRbKo4cctEglBbD2v66ugrYh/sS2DqRVoA9hAT54eVnYn1VEZEEprVxdnIiIiLQoCrQtzY6fYMNnMO5hl5YRGeLH4rvOKHv94uX9KX3Lwa60PAVaERERaVAactAShcfAgGtcXUU5/WLC8bZqDK2IiIg0PAVaaTJZXq3o99vfsW35wdWliIiISAuiQCtNZu1pz/ArJ1Hy6c3YZp2MfbXrx/qKiIiI+1OglSYzfexJrI+6kNXF0WzLgqwfnoQdC11dloiIiLg5BVppUmuCT+fKoju5tuRODlnbwS/PubokERERcXMKtOISB2nDfK+hFJTYXV2KiIiIuDkFWnGJ0+MjsdkNO1PzXF2KiIiIuDmPDrSJiYlMmTKFwsJCV5ficXq3D2VE9wgMUGJz8MVOO68v3okxxtWliYiIiJvx6AcrJCQkkJCQQHR0tKtL8RiXDY4lwMfKeae0J/83C7GlO8n8cRYjk5eSkxxI8ZB38ff1cXWZIiIi4kY8OtBK0xvdoy2je7QFYG6HCWRt/52zlv2byw7/JBbZikGBVkRERGrBo4cciGvZrf6sCRhSbpkl7yCkbXdRRSIiIuKOFGilWfF9YxS8OAAOrnd1KSIiIuImFGjFpXKLSsu9tpQcnvUge58LqhERERF3pEArLrU0PZhUWnHAr8vRheFx8NGlsOfXo8v2rYL/6wKfTm/6IkVERKRZU6AVlzmpQxjekd15deC3rOz5z6MrrvoMYgbDvPsgdatzWdoWKEiH/atdU6yIiIg0Wwq04jK9O4SyYMYoHpjYu/wKiwXOfQbspbB9gWuKExEREbehQCvNgt3iS6oJwwS0IscRyG7vLtC6M9hLwGGHzN2uLlFERESaKc1DK82C3erP0OL/sP6fZ3H56ytZn5zDr/3a0GHBQ7AuEdK2QccBUJzr6lJFRESkmVEPrTQbNrzB6ktmvnPmgzU9Z0D/qyFlPfS+AMbc7+IKRUREpDlSoJVmxeYwZV8bL2/wDgBge2oeP29NpdTucFVpIiIi0kx59JCDxMREEhMTKSwsdHUpcthJD80DICyg/ONvNx/M4Ye0fXR2FBHrisJERESk2fLoHtqEhATmzJlDQECAq0vxeAHecNGpHRndI5Lf7xtL18ggnvhuM5+ucT5goV2oPzec3gVzgv2IiIiI5/HoHlppPqxeFp69pF/Z6ykDY/hu/UE4UMUGKRudMyB06FdFAxEREfEUHt1DK83XpYNjefcvg+kYflzvuYHnvlmF/dWRmNdGc+eb35KRX+KaIkVERKRZUKCVZi12+CXsCh9K1NDLALDbbZT8+ipWY8OCIXTnN+xIvB9HkabzEhER8VQKtNKste93Fp3v+J6YoZNxWP3p4nWQu3zm8GTppRQbbx7w+YBBu2ez7qf/ubpUERERcRGNoRW3kRM5gEtL7ifVhLHDdOQO77ll60odul1MRETEU6mHVtyHxcJyR292mI4AzLRdRvYp09jl3cXFhYmIiIgrKdCK23rHPoGMkQ9TZPF3dSkiIiLiQgq04jb8fawA+Fr1YysiIiJHKRmI2xjUqTX/mzaE/00f4upSREREpBlRoBW3MqRLG7pGBFe+Mu8Q/P46ZO5p2qJERETEpRRopeVY+hx8+w/46VFXVyIiIiJNSNN2SYvQadfH4FvqfGEcri1GREREmpR6aMXtWK0WvL0sAPh6e/FB8HXYrf5QkAExGl8rIiLiadRDK24n2M+bL24ZTqnd0DE8gC2+vVk+9GwGdWrNni8f56TiJEJcXaSIiIg0GQVacUt9OoSVe+0whhcXbidkSyomNJtvP1/HgLhWXHRqtIsqFBERkaaiQCtuL8Tfm799/AcAdwb7kJpXzAfLk/hle7oCrYiIiAfw6ECbmJhIYmIihYWFri5F6uHZS/qxO72A+Khg/vjfcg5tc3VFIiIi0pQ8+qawhIQE5syZQ0BAgKtLkXoID/SlX0w4gb7e9GgXQp+QAqYPCCa/2MaN76/i73P+wO4wri5TREREGolHB1ppeVr3GEE3awrTM5/hUG4x3284yNzV+ygstbu6NBEREWkkCrTSssQNg3EPY7UVuboSERERaSIKtCIiIiLi1hRopUUK9LUyrlcUD/fcyxCvja4uR0RERBqRR89yIC2Xj9WLNya2gv/czdU+FgpKpoFf2Ik3FBEREbejHlppmewlsPZDALwsBozDxQWJiIhIY1GglZYntAPs+x2WPE3xkNtdXY2IiIg0Mg05kJan8+lw9x7wCaQ4Lxe/5c+7uiIRERFpRAq00jIFhLu6AhEREWkiGnIgHmF7ap6rSxAREZFGoh5aadH8vZ1/s+W8ezn2kENYo3rC+f+B8FgXVyYiIiINRT200qL5Hg60p3v9yUuFZ1KavgeSlru4KhEREWlICrTiMX6xDibbu3XNGi95Bt6/CDJ2NW5RIiIiUm8actCSZO6B5a8AFldX0nz4BEK3cVBSQAfvTuzcnk/qgRx69T3Bdms/hPTtsPc3aN25SUoVERGRulGgbUm2/QD5h+CCl11dSfNh9YEr5wLwLLDxMW+2HMylq81RNhxBRERE3Jv+RW9pWneBmEGurqLZahXow7pd+/jvb3sAsNkdfLE2mU0HclxcmYiIiNSVemjFo7Tv3JsH1r4B89/g3XX3sinybP63Yi9dIoP46eZ+YPEC/1BXlykiIiK1oB5a8Sznv8hPF6zg18CxdE5bxLcrNgMQZsuAWb3h+VOgtNDFRYqIiEhtqIdWPIuXF2NOjQe/qRR9disP+7zLb45ehNoDoTTf+WEvKb+NrRiSV0HUSeq9FRERaYYUaMUz9T6ftatXMWn7c0yyLoVjM+y+FTgKs/ECjDFYlsyCn5+AflfChS+5qmIRERGpgoYciMeyHJ7e7A9HF97gInYOuN+54oPJrCvtyFZHRzbsz4GSw4/NPfx5T3o+aXnFrihZREREKqFAKx7rlJgwADq3a8NvXW5hyopuh9cY7iu+igxCKS3MLbfNpgM5jH56Eec8v6SJqxUREZGqKNCKx/L3tgIQ6u/Dw+f3odhmytad1y+GgsBoTl33b1j3SdnynMJSjIGswtImr1dEREQqpzG0IkDH8AASbxvH5m3vEOxVwvTh47n01WDivbyI3j/P1eWJiIhINTw60CYmJpKYmEhhoXtO05ScVUg3INjPo7+NDaZnu1Bod1HZa2PxwlisLqxIREREasKjhxwkJCQwZ84cAgICXF1KnZTaHAD07qCppJpCsc3B60t2uroMEREROY5HB1qR2kjLL2Hj/hweuaCPq0sRERGRYyjQiufqNha6nwX9LqvxJh3D/Djb9hMjLH/AR5fDOxOhKLsRi6yB3b/AqnfBbnNtHSIiIi6iwZfiuaL6wBWJ1TZZHjSWktZ5tA8PxFJkOKNoPpE/PsdbVmDL4UY5+yE7GXYfnsqr9wUQ0q5RSy8ncSrkH4KQ9hB/VtMdV0REpJlQoBWpxp1/tgduZHbAMsamvMEN9h8paj8Y/wO/H22UsRPz+2tQkIGlJA8ydsLZT0LeIfj4SgiLgclv4nAYDGD1sjRskcZR/rOIiIiH0ZADkRrYkpKLt72Q7T7xpI6dVbbcHn0a5uOrKNn9G/9IGsaONmccDZbpO2Dvb7DxCwAuevkXej34PVtTcis7hIiIiNSRAq3ICUw7vQu07U2mCWaHTzzGJ4gi44PDN4SJGXewwx6Fn8M59Vv24Qcu7ErLZ+bcpeX2systnxKbgwPZRY1TaPZe50MgSvIbZ/8iIiLNlIYciJxAq0BffHqdyak7oxnYrhVPBUUxsvh5fI0PfqGBFYcQFGQQ+Mll/DPnZ3ZZoulMSsMWlL0PvrsbWnWC8Y8dXf7tnYCBMQ/A6f9o2GOKiIg0Y+qhFamliGBfOnXqTJe4OL6/43QOWdvhMBayfNuyak8GrP8Er9RN3FhyB7dZ/kmDjmzduwKe7QObv4ZlLx238vCjex2a7UBERDyLAq1ILYX4+5B44zDev+40fL29uD/gPvoUv8nYsxPoHhUCwGZLVyZfeTMxrQKw2R38uj2tYQ6eewDa9YXb1py4rTGwdR4c2gyFWa6fXkxERKSRaMiBSBWODCWwnuDPPofFSiH++Hp7EeDjfFRusJ83I3tHMTKiH14vW7j8jd+IJJPulnxgsHNDeykcXAcR8eAXXPPCvKzg5VP1+oJ0WP4qGDvMuxf8jjxJzgJ3/AkB4TU/loiIiBtQoBWpwiMXnMTK3Rmcd0oHlmxLBSC6VfWPST4lOhwOQI92zp5aP28vzOFg/JnfQ0Rb0lid1hm6jYPFT8HPT8Ipl5N79gv4envh522tW7EDpkJBBhRmwu+vlV9XnHP069JCBVoREWlxFGhFqtCtbTDd2jp7Tiec1J4N/xqPn3f13bX+Ps71gb4Vg2kIBQB8sXwTXTJ+IXzlcxAaTWZmGgP+9QP9Y1vxyU3D6lbsgGuhQz/44q+wewnz7f2xtophTN8usPTZuu1TRETETSjQitRQkF8d3i4+zh7dL33vww/nlF7FNgdZB3cSPupuCGhF8Z8LcBjYn1VYcfuFj8PKt2Dcw84xsKUF1R/PLwyAHaYjK1rfypieXgq0IiLS4inQijSmkHaUXL+Ivq8NL1vUOtCXHal5BLV3YLGXsDutmnlj962A/FRnz+sRHU6tuv3YB/igYBDP/g4jAMI6QptuEBQJScvqfToiIiLNkWY5EGlkpk08i+0ns9bRBVtAJJefFosFC1/8sZ8XF26n2GbnkQv61G3nq98v/yAFnwBSgntTjK/zdUg7uHUVTP22/iciIiLSTKmHVqSe/n3hSfyxN5uzT2oHP1fSwMvK1aX/BGBLm4eJbhXAAX9vMtNK6RvdmhHhkQR3CC2/ja0Eq/1ETxQz8OUtMORmaNurVjXnFdvYlZpP7w6hFR8MISIi4mYUaEXqaVjXCIZ1jajVNt3aBpMX1Io+nSOw7v+DjiufZIw9CBjrbPDmOMYcXA+BrU+8s+F3gLdfjY/9f99v5uVFOwC4/9xeXD+yS9m6zPwSvK0WQvyrmRZMRESkmVGgFWlqdhutAn05IyYSfP1h12LasZhbiAAedLbJSsLL2J3jZ+vBGMP8jSm0CvRm0OFlP29N5e9nxrM3s4CcwtKytknpBYyb9TOhAT78fu9YvNRzKyIibkKBVqQhdRoB2xdA1zFli3ytXlwyMIbswlKsjo7w0aUQ2hHadIXQDgCUBLTFUmiH32aDl7fzKV8NYMP+HKa9vwpvL8N236PLe7YPJfm4WRWyCksosTtIyyumYY4uIiLSNBRoRRpS/HjnxzG8vCw8Obmv84XtY3j/Itjzi/N17/Phzp3sWvcbPb6/DL67q0HLsTmc0dTuUEQVEZGWS4FWpCl5+4H1uPGpQW0wRx5la7HC1V9AUTY7P76bLuyr9yH9fbwI8fUGO+QUlVZYvystn8e+2Uh4oG8lW4uIiDR/CrQiTe2Uy8DqB93HV1jlAD44GMNVQ0aw3fIS7R2HSDGt6OSVQkHHYfgGhvKzvR+2PYbxp14FXtYT3jgW4u/Dr3ePhkfhv78lVeit/XVHGgs2HWrAExQREWlamodWpKmdcilcMQfa962wyuEwPPjFBtLzS3jY6xYWTPoD4oYCsK6oLQtOeY7rNvblwW+2wgUvwnnPV+zxrYT34Ru8Pvp9D5sP5p6w/YrdGbU8KREREddRoBVphrIPzz4Q7OdNpzZBVbbLLSolM7/khPuz4Ay0gb7Vh98QP2/G94ni0teWcyjnRPPgioiINA8KtCLNTGzrQM5+fgkZR/Jkm64A7HS0IyP/6BhYu8NwxtM/c9rMH9mbUdAgx/bz8WL2VQOBozeUiYiINHcKtCLNzM93jibAx0qR/fCCETNYdOkWnskZx0Nfri9rZ4whLa+YEpujrEf3WOZwm8oE5CUxMONrNu9J5tft6ZW2+W1XOu8v30Op3VHfUxIREWlUuilMpJmxWCxYLOUWMLpnO24dW8RDX26odJtDuUVAWNnriGA/ftp8iG2Hjhkva/Vltv1BvvMeSK9VGYxI+QW/Vtfwjf81XNw/mrmry8+o8LeP/wAgKsSPs/q0a6jTExERaXDqoRVpBrp060VmQBwZkYNrvW3PdiFc/+5KVu3JLFt2Zu8onk44hb0Zhx+e4OUF1/+IvfsErvFfTJsU5zy4550cxatXDWD6qC6EB/rQu0NYhf1r4IGIiDR36qEVaQZ8W0fje/efddr2vesGc/Wbv5NVUP7msEGdWnFSx1D6Roc7F7TvS5crnoNXV8HB8seKjwph7YNn1en4IiIirqZAK+ImhneLYGzPtoQH+vL9+gOMeeZnAn2t+HlbK20f1yaIr28dWXFFm25wcB2Ex9TouOv2ZbMhOZtLB8fSITygPqcgIiLSKBRoRdxEt7bBvDl1EHvS8/lm3X4cxrDoztGEBRydiqvU7iCroOINYuVMfsv58dVtNTruiwu3A2A3hoQBMbQL88ffp/IQLSIi4goaQyviZuLaBLHmgbP48e+jaBviX27dMz9s5cPfk+gRFVz1DiwWyt91VjMfLE9i9NOLuPfTdbXeVkREpDGph1bEDQX4Vt5DmpFfzK1jujF1eOcGP+aRqcHSa/AgBxERkaakHlqRZmhszyhigp3DDGrCz9uLx77ZxJYaPNa2Js7sHUW/mHA6hDl7gK2HH5175LOIiEhzokAr0gw9M+UU7h/kTUzrwBq1/89l/fH3sfLHvuwGOf7rVw/k878Op02wHwB/G9edV68cwD/O6tEg+xcREWlICrQiLUBsm0AiQ/xqv2FpAWz6CnIOVNssLMCHCSe1IzzQp9p2IiIirtDiAu2tt97KyJEjefzxx11dikjzt/wV+PhK+PYfNd4kJaeI7YfyGrEoERGR2qlzoM3NzeXbb7/ltdde4/333+fPP+s2KXxDWrlyJd7e3ixZsoTVq1eTkpLi6pJEmjdjd3621+xGr0GdWgHwt4/XNlJBIiIitVfrQLtnzx6uueYaunbtyjPPPMPPP//M559/zqWXXkrPnj156623GqPOGvntt98YM2YMAKNGjWLVqlUuq0Wk2RtyM4z4GwyeXuNNurUN4a4JPSi1OxqxMBERkdqpdaC96qqruPjii9m/fz8//vgj//3vf5k7dy4bN27k+++/Z9u2bbz44ov1Luy2226jU6dOWCwW1q5dW27dtm3bGDZsGPHx8QwaNIgNGzYAkJWVRWhoKAAhISFkZWXVuw6RFqttLxj3MLTt6epKRERE6qXWgXbx4sWcf/75eHtXnMK2U6dOzJw5k1tuuaXehU2ePJmlS5cSFxdXYd306dOZNm0aW7du5e6772bq1KkAhIeHk5OTAziHRISHh9e7DhGPYAxs/gYOri+32OvwNF1emq5LRESaMYsxxri6iOp06tSJzz//nH79+gFw6NAhunXrRkZGBt7e3hhjaN++PUuXLiUzM5OPPvqIWbNmkZCQwIsvvkhUVFSFfc6aNYtZs2aVvc7KymLu3LlNdUoUFRXh7+9/4oaVsDsMN//sHPf4ne899PJKYkOXaeyLGkfMwXlEZK1hTc97GrLcJlGfa9JS1faavPCHnQ0ZhomdvDivc83/Vo1OmU/vnW9gwVDsE8bykx6jyL8tAPvyDLtzDIOjLPhanaF2XZqDz3c6eGBw0z6XRT8jFemaVKRrUpGuSUW6JuW5y/W47rrr2LdvX6Xr6vUv0saNG+ndu3d9dlFre/fupX379mU9xBaLhdjYWJKSkhgzZgzvvPMOI0eOZPz48ZWGWYAZM2YwY8aMstfR0dGMHz++SeoHmDdvXp2PV2p3wM/flVvWp08f+gwYD78nwfbkJj2XhlKfa9JS1faafJj8O2Sk0q1bV8aPi6/5gVYmw04Dwe3w8w9l1IZ74J4k8Kr8aWQ+m1NYkLqF8eNPr/kxGoB+RirSNalI16QiXZOKdE3KawnXo16B9o477uCHH35oqFoaxEsvveTqEkTcU3AkXJ4IszSmVkRE3EuNAm1oaChDhgzBGIPF4vxvR2NMhZu1mkJMTAwHDhzAZrOVDTlISkoiNja2yWsRaU4m9e+I1cvCuF6V/89ElaIHQft+ED+hVpsVldr5yzsrsDsMb187iEDfph2CICIickSN/gWKj49nzpw5FW6yOvPMMxujpmq1bduW/v3788EHHzB16lTmzp1LdHQ03bp1a/JaRJqTC/p15IJ+HWu/YbuTYfrPzq9P8MSwY+UUlfLrjnQAMvJLFGhFRMRlanTnyPz58wkJCal0eWOZPn060dHR7Nu3j/Hjx5cLrLNnz2b27NnEx8fzxBNP8PbbbzdaHSIiIiLSvNWoS6VVq1blXv/f//0fd911V6MUdMTs2bOrXNejRw+WLVtW72MkJiaSmJhIYWFhvfcl4klyi2xsS9Hjb0VEpHmo06Nvv//++4auwyUSEhKYM2cOAQEBri5FxG30bBdK6yBfrn7rd1eXIiIiAtQx0DbzqWtFpBF1CA9g5qSTsTv0e0BERJqHOgXaIzMdiIhnCvB1zlHr7+P8FXL33D85c9bP7EjVMAQREWl66qEVkVrrGhnM17eO4NvbRmKxwC/b09l2KI9tKbmuLk1ERDxQnQLtyy+/3NB1iIibOaljGF0ig8stS8sr4cdNKRSV2l1UlYiIeKI6BdpevXrx3nvvkZOTA4DNZlOvrUhLkrm7Tpv966sNXPfuSmb/vLNh6xEREalGnQItwOOPP05oaCiFhYV069aN9u3bs2TJkoasrdElJiYyZcoUTdslcoR/qPNBCy8Pgfy0Wm9eanf+YVtsUw+tiIg0nToH2uBg5381fvvtt4wfP57ly5dz7733NlhhTUHTdokcxzcIblwK9hKwFdVq09E9IunTIZRTYsIbpzYREZEq1DnQ+vn5kZKSwv/+9z8uueQSOnXqRH5+fkPWJiJu5NYx3fnmtpGc3DHU1aWIiIiHqfPD1x955BFOOeUU2rdvz6hRozDGKNCKiIiISJOrc6AdO3YsBw8eLHu9ZcsWzjjjjAYpSkRERESkpuo85OB4PXr04NVXX22o3YmIm+gcEUSwnzdtQ/xcXYqIiHioWvfQ/vbbb5x22mlVri8sLGTXrl307t27XoVJLeWlws5Frq5CPNCPM0YBeoKgiIi4Tq17aJ955hnOPPNM3n77bTZu3Eh6ejrJycn89NNP3HXXXQwdOpSUlJTGqFWq8/tsOLgO+l/t6krEw1gsFoVZERFxqVr30M6ZM4cVK1Ywe/ZsHnvsMfbt20dQUBB9+/bl4osv5pdffiEoKKgxapXqGANdz4Ce57q6EmkJ/ELh3fPg2u8gpJ2rqxEREalWnW4KGzRoEIMGDWroWppcYmIiiYmJerCCyPFu+hVmnw7ZyfUKtDlFpTwzbwvd2gZz1dBODVefiIjIMRrspjB3pAcriFQhPAasvvXezao9mby7bA/PzN/aAEWJiIhUzqMDrYiIiIi4PwVaEana77OhKNvVVYiIiFSrXoH2wIEDLFq0CACbzUZJSUlD1CQizcHZT8KOhbBrSa03XbQllVV7MhuhKBERkYrqHGg/+eQThgwZwtSpUwHYsGEDF154YQOVJSIu1+dCCO0AB/4Ah73Gm/1leGeC/b358LekxqtNRETkGHUOtDNnzmT16tW0atUKgFNOOYU9e/Y0WGEi0gzET4Dlr8DaD2u8SZfIYEZ0i2jEokRERMqr07RdAFarlTZt2pRb5utb/7uiRaQZOeOfzh5aW5GrKxEREalSnXtoQ0JCSElJKXtC0I8//kjr1q0brDARERERkZqocw/tk08+ydlnn83OnTsZMWIEu3bt4ptvvmnI2hqdHqwgUgMWL1jxJnToD9EDXF2NiIhIBXUOtAMHDmThwoX8+uuvGGMYNmwY4eHhDVha40tISCAhIYHo6GhXlyLSfJ31b/jir7DlWwVaERFpluocaNPS0oiIiODss8+usExEWpA2XSGyh6urEBERqVKdx9CeddZZNVomIiIiItKYat1DW1JSQlFREXa7ndzcXIwxAGRnZ5Ofn9/gBYqI+/GywC/b09h+KNfVpYiIiAeodQ/tzJkzCQ8PZ/369YSFhREeHk54eDgnn3wyV1xxRWPUKCLNwfYFkLy6Rk2vHtaJv47pxuSBMfzf5L6NXJiIiHi6Wgfahx56CIfDwbRp03A4HGUfWVlZPPjgg41Ro4i42mk3QlE2vH4GLHryhM1D/X24akgcVw2J46QOYU1QoIiIeLI63xT28MMPc/PNN7N27VqKio5Our56dc16cETEjbTtBbeugp8ehX2/12pTH6uF3CIbM+asxdfqxYHsIp69pB+tg/QgFhERaRh1vins+uuvJy4ujrS0NP71r3/RoUMHzj333IasTUSaEy8rtIqr9Wbdo0J46fJT+XR1Mv9bsZfF21LZmZrXCAWKiIinqnOg3bt3L3fffTd+fn6cd955fPrppyxYsKAhaxORFmLCSe358PrTeO8vg2kf6u/qckREpIWp85ADX1/nfxf6+/uTnp5Oq1atSEtLa7DCRKRlGdZNc1SLiEjjqHOgjY+PJz09nSuvvJLTTjuN0NBQBgzQU4REREREpGnVOdB+8MEHANx+++0MHDiQzMxMJkyY0GCFNYXExEQSExMpLCx0dSkiIiIiUkd1GkNrt9u5++67y14PHz6ciRMn4u1d53zsEgkJCcyZM4eAgABXlyLiHry8Yd9KePtcmF/3afp+2JhCic3RgIWJiIgnq1OgtVqtLFy4sKFrEZHm7uQpcM7T0GUUrPmgTrv4y4jOfLxiLws2pTRwcSIi4qnqPMvBOeecw2OPPcb+/fvJyckp+xCRFszbF/omQPz4Ou/i+pFd6BwRxOGnZouIiNRbnccIPPLIIwA88MADWCwWjDFYLBbsdnuDFSciLZdDiVZERBpInQOtw6HxbyJSN1Ghftz60RqC/bw5o2dbV5cjIiJurs5DDkRE6uqFy05lVHwku9PzXV2KiIi0AAq0ItLk/Lyt+Pvo14+IiDQM/YsiInVTUlDnmQ5EREQaUp0DbWWPudWjb0U8RGRPGDAVvvgr2EtdXY2IiHi4Ogfas846q0bLRKQF8vaDEX9zdRUiIiJAHWY5KCkpoaioCLvdTm5uLubw1DvZ2dnk5+sGDxGPYxzgsIOX1dWViIiIh6p1D+3MmTMJDw9n/fr1hIWFER4eTnh4OCeffDJXXnllY9QoIs3Zi4NgZgxk7nZ1JSIi4qFqHWgfeughHA4H06ZNw+FwlH1kZWXxwAMPNEaNItKcZe2B0nzIO+TqSkRExEPV+cEKr7zyCg6Hg4MHD2Kz2cqWx8bGNkhhTSExMZHExEQKCwtdXYqIiIiI1FGdA+27777Lrbfeire3N1arc+ycxWLh0CH36aVJSEggISGB6OhoV5ci4nECfKy8tHA7cW0CGdMzytXliIiIG6vzLAePPPIIK1asICMjg9TUVFJTU90qzIpIPfmHQffx0G0c+IdDxk7Y8BnYSmq0+cPn96FfTDi/78ps3DpFRKTFq3OgjYiIoEePHg1Zi9TCdSM6Exrg4+oyxJP5+MMVc+DKueATCJ/dCIlTYW3NHrYQHuhLh/AAAGx2R9mMKSIiIrVV50B74YUX8txzz3Ho0CFycnLKPqRpTB/VhY6Hw4BI83A4kNpt1Tc7zjfr9tPtvu+4//P1jVCTiIh4gjqPob3vvvsAmDFjRtkyi8WC3W6vf1Ui4jH2ZjhvytydrnmsRUSkbuocaB0OR0PWISIiIiJSJ3UOtElJSZUud6dpu0RERETE/dU50A4YMACLxYIxhqKiIgoKCmjTpo1mOhAR56Nwk1dDRHcICHd1NSIi0sLVOdCmpqaWe/3pp5/yxx9/1LsgEWkBfn8Nvr8Hep0Pl7zv6mpERKSFq/MsB8ebNGkS33zzTUPtTkTcWXFu+c81sCs1n4GPzufKN35rpKJERKSlqnMP7bFTdNntdn777TdN2yUidbY/uwiAP/dlubYQERFxO3UOtOHh4WVjaK1WK927d+eFF15oyNpExF34h0HufgiLgQUPQ+vOtdq8b3QYvlYvIoL9+HVHWuPUKCIiLZam7RKR+rvmKyhIcwbaL26GjV/UavNukcHMuqQfmw/mKNCKiEit1TnQHrF//34AOnToUO9iRMRNBUc6PwACI1xbi4iIeJw63xS2adMm+vTpU/Zx8skns3nz5oasTURERETkhOocaG+++Wbuu+8+MjMzyczM5L777uOmm25qyNpExAPZHYa9GQXszypky8Gaz5IgIiKeq86BNjMzk8svv7zs9aWXXkpmZmaDFNVUEhMTmTJlCoWFha4uRUSAtiH+dAgP4KxnFzP+2cWMf24xG/Znu7osERFp5uocaK1WKxs3bix7vXHjRqxWa4MU1VQSEhKYM2cOAQEBri5FpOUIjnJ+DmxT601bB/kyZ/pQCkvt5BbbAMgvtgOQXWx4f9lu9mYUNFipIiLSMtT5prDHH3+c008/nb59+wKwbt06/vvf/zZYYSLipk7/B/S7HLbPh41fVtv07JPak5xZyHmnnPim0u+THPz06wYmnZrFrEv6NVCxIiLSEtQ60Obk5JCRkcH48ePZtGkTv/3mfKpP27Zt6dmzZ4MXKCJuxssK4THgdeJfL0O7tmFo15r15Brj/Ow48oWIiMhhtR5ycNddd7Fq1SoAIiMjmThxIhMnTmTfvn3cfffdDV6giLix0oLyj78tyIDsZNfVIyIiLVKtA+3vv//OxRdfXGH5pEmTWLx4cYMUJSItQJvucOBP+Oiyo8teHgLPnQyHNMWfiIg0nFoPObDZbFWu8/Kq8z1mItLSxA2FS96HL2+DdyZCQCvIOwQYKMpydXUiItKC1DrQlpaWkpOTQ2hoaLnl2dnZlJaWNlhhItJC5O53foiIiDSSWnepXnrppVx11VXl5pzNzMzk2muv5dJLL23Q4kRERERETqTWgfb+++8nPDycmJgYTj31VE499VRiYmIICQnhgQceaIwaRcRdBUc5ZzsIOfG0XCIiInVV6yEHVquVd999lwcffJDVq1cD0L9/f7p27drgxYmIm2vfF+7eDUU58GxvV1cjIiItVJ0frNC1a1eFWBE5Mb8QsBW7ugoREWnB6hxoRUQaQ1iAD3eO70GJzcHHK/a6uhwREXEDmmdLRJoVLy8Lfz2jG387Mx6rl4UVuzNwOPR0MID0vGK+/GM/mfklri5FRKRZUaAVkWbrwlM78PLC7fyw8WD5FdnJUJLvmqJc6OkftnDbR2t4/sdtri5FRKRZUaAVkWbrzvE96dEuhFL70R7annm/O28w++8UF1bmGkeuQ6nd4eJKRESaFwVaEXErIfbDc2Dnpbi2EBERaTYUaEVERETErSnQioiIiIhbU6AVkaZ3cB0UZMD2H6E41/k575CrqxIRETeleWhFpGl1HQvz7oOf/g1F2eAfDkVZEH82XP6/SjfZsD8HeyUzd63ak4Gft5WTOoY1askNYfnOdFoF+tKjXYirSxERaXHUQysiTWviLGh3sjPMgjPMApQWlG+38i1Y8C/Oig/jv8v3sPRA+URbandw8SvLuPClXygqtTdsjcbAspdg0RNgt9V7d3/uy+LS15Zz8Su/NkBxIiJyPPXQikjTO+Ne2PUzZO6GjV9U3ua7u8Fewo3XnskfByP4bn35uWiPxFubw+AwDfzghZI8mHev8+veF0LbnvXaXbHNcfhzAwdvEREB1EMrIq7QbSyc+QgEtT26LCcZXh8Ln053zjHrsIHF+StqRPbXfOd7D6O91lS5y1nzt3L568tJSi+osk29bZ0HrwyHVe823jFERKTWFGhFpHlI3w7JK+HP/8G2eXDN1xAaDUCvgpX08kpiiNemKjf/fE0yv+5IZ83ezMapzxjY+TOkrIcdPzqXOezg0EMORERcTYFWRJqnjv2rXe2o5SiDjPwSsgtK617Poidg+UtHe5XTd8DMaHh1+Ak3zTrBcY0x7M8qpMRWMRznFdtIzS2uU8kiIp7CowNtYmIiU6ZMobCw0NWliEgNWDCkH0wC4EC2830bSSaWnORqtzuYXcSwJ35k1NMLj95AVpwHqVudPa81kbEDRv4dht3qfJ170HkjW/oOyE2B7H2VbrZ8Zzo3vLeSuDaBVe767V92M+yJn/jXVxsqrJv08i8MfnwBf+zNqlmdIiIeyKMDbUJCAnPmzCEgIMDVpYhIDVxqXcjNjo/Y7IjB4TAEUMRCv7/j/8pA2jgyqtwur7iUolIHWQWllNoP94J+Nh1eGgRrPqh5AX4hZeN6y9iL4cVB8Hw/yEqqsElaXjGnxobz/l9Oq3K3qXnOHtjKemJTc4sxxtnDLCIilfPoQCsi7iXMUsBXjqE8arsSAB9sBFuKsNhLCKCodjsrPDzWtrDqIHxEcvJeUg/upfTIZLj56ZCferRBcTY4Sp29viIi0uQUaEXEdWKHQKtOED34hE3XOLqxx9GWFY76TaFVF4fevw4ObeSHrI7Osb1pW509vCIi0iwo0IqI65w8GW7/AwZcc8KmX9uHMKrkOT60j63z4XpYkvA6UMnUX3Yb7PjJ+QjeSoYN+DoKebz0CpLCBkLcMJgwE2y17BEWEZFGowcriEjj8wuBLqOdwTEwolEOYQ4/amHh5kOM7tGWsAAfikrtfLB8Dyk5RQRSxFe+9+H7rh3uWFd+41Vvw4KHnY/h9Q+D6+Y1So3H27A/m6Xb0sgq0PhYEZH6UKAVkcbn7QdXV/FEsHoI8LGWfX0gu4jrB4Syd+svJK4I4frTu/H9ugMsWfgd0Z17cMOIOHxXHp7hwHbczVelhdD9TBh4HXx5a4PXWZUnvtvMkm1pTXY8EZGWSoFWRNyW1ctS7vUtmU8RXvozS5ILgTtpd/An3nXcCwWnkHPeZ7CygQ7ctje06kxRQDv89y+r824a+om9IiKeSmNoRaTF8Cp1zjIQXHQAHA68bfnOFcfNPmCqSJIOY8gvKmLF5t3VHyiqN9y+lrQxT9e51j/2ZrE/q25zYG85mMvO1PLntHDLIT5bs6/KcxMRacnUQysirte6K1j9IDwW0redsHlMqwCCS725qn8cLC+/LsUnhlP3vAm/xgJ+ZcuDfI8OT0jOKiS6kv3+mRNEl4Isun16NlgqadCA7vrkT9oE+3LdyM4s2pKK1WLBUYMwOrZnW5KzCnniu81cHOVcZrM7uPbtFQDER4XQp0NYY5YuItLsqIdWRFwvbig8cAimvFuj5iF+3kSF+HPTqK4V1n3T5hrWRV9efp5Yyg9PqCo4FoZ04oKSR2llya1F8XXjMIa/ntGNK06L4/WrBzKkS+sabdc3OpzLT4ut8tG/6qAVEU+kHloR8Sg5JpCouZPAz7/S9fnGnxLjjbevH5bSAjLyi7FVlR6b0I7UPIVVEZEqqIdWRDzKk7GzSS8CcpIrXX+IVowunsWowqfIN35Me28lNruhXVjlAbgpDO7cmv+bt4Vv1u13WQ0iIs2ZAq2IeJTHrhiNw8v36IK1H8Le38q12U8Ee23hAFxU+g19fZNpHx5Y+4OlbWPA8lsZU/hDPSqGh87rzWmdW1NU6qjXfkREWioFWhHxXKPvhaBI2Px1lU2u9P6R1M4X8kfI6bXf/7b5tN+/gLMKv69HkSIiciIKtCLiNvx9nL+y/I+ZsaAuLIfvD/vXLwXsD+h+wvYZHc/A5uXH9+sPMPyJn3j7l10nPkhxLhz8s9wiu8Nw+evLOfeFJWQWlNaq5iBf5y0PQX71O3cRkZZIN4WJiNu48rQ4zurTjl4FPvBT3fcTFeoPWRAR7Mcan/50iF4P8WdXaPepfSRdLPvxDY+H5CL+2JcNwIrdGVw7vHP1B/nledj+IylRp0P6IQAcBn7dkV6nmp+4+GT+MqIz/WPD+fD3pDrtQ0SkpVKgFRG3YfWyMLhza9hRv19d3oen8Arys7KvzTCYdJVzxXFh80HbtQD8NyAS2Fu7g9hLoM+F7KMnwemv1ategPBAX+e5i4hIBRpyICIt085F9F33OIWWgKY97k+PwtM9YPvRLuQ2jjTavz+C93xm1mgXS7alsj45u7EqFBFpcRRoRaTZiwzxo5UfDO8WUWWbfjHhBPgcM740ZT2Z4Sfxctjf63zcc09uz2mdW9O9bXDNN9r7G+QdhJR1ZYsiHGn4ZO1giNfGE26+bl82V735O5Nf/bUuJYuIeCQNORCRZi+mVQDj/LzoGR9ZZZvnLjkVPveCY2a2KvENJ99+OIzu/LnWx3180smEBfjUfINdi6t9VJcVBxd6LSWbIBY6TsU/by/8sRB6TTxas93u/GzTFF0iIjWlQCsiLV/GDki8Bk65HHzqMJ9sDZScdAm+Cx4GW2Gl6+0BbfgurxuXei9koGULE0qeoPvKV+HAYsh9GCwXNkpdIiKeQEMORKTF2pd5TLj0DoCLXgFr4/wdX3jGvyE8psr1xtufW0pv59KSB8gjAG8cWBw258ojn4/hMPDGkp1U97Rbm8Pw3IKtfLTi6A1rHyzfQ0FJxf2JiLRk6qEVkebDcmQMrOXwR/0UlNi5cFgsLAC86jd/61+Gd6ZNkB/5xTZyi6uYQ9ZSs2P4+1i5pG80YbmVD2doG+LH1UPjePSbTdXuJykXnlu9rez1P8/uyezFOzmzdxRje0XVqBYRkZZAPbQi0nxE9oAp78FVn4KPf713FxbgQ99hZ8OkN+Dyj+u1r1NiwnnwvN50j6rmBrFJs+GCl6DPRdXuy9fbi2uHd8bqVXlot3pZmHZ611rXeNGpHZ1z7IqIeBj10IpI82GxQO8LGnafXl7QN6Fh91mV9qc4P3YtbprjiYgIoB5aEWnOAlvBh5fAgT9O3HbONbDvd/K9Qskpqt1jZQ/lFDHzu00s25FW422KbXY+W5NcbZvCEjuL9jpnLXD466EIIiKNRYFWRJqvq7+A8DjI2nPitinrYNLrbA4axJaDuQB0iQyq0WE+X5vM7J938sJP22tc2tqkLL5df7DaNjvT8vkiuzvLzp1PyoVzarxvAB+rhQcn9ua2Md3o3jakVtuKiHgaDTkQkeYroBX41OJJXyHtyr1sHejbwAWVFxbgDUXVt+ndPpShgwaQlF5QfoWt+g0tFgt/GdG5nhWKiHgGBVoRcX+h0ZCfCkFtgbwmO2y+JRDjF0pakQUfb78ab+cIjcHrrfHgH954xYmIeBANORAR93fTUrh7N0TGN+lhsyxhFN2+mRHFz9fqgQ05V34PrTpDQc3H7B4vs6CEnJKqZ6nNL7axbEe65qQVEY+gQCsi7sk4wHH48bBWX/BtnCeAnZC3P8WceGhD62BfTo0N57TOrQkKDKjXvLgndQxjb0YBb2ys+vG4T83bwmWvL+eZH7bW+TgiIu5CgVZEmrfQDgAU+bU5uiyorfNGsddH13GfHQHI9o6oZ3FV7d9Zc4H/0YcbBPt589nNw/l4+lD8vOv3kIf+sa145IKTKK06z5b1zKqHVkQ8gQKtiDRvk16HO3eQ0mbo0WXtToJrv4espLrt84pP4K5dbAs8tWFqPN6YB+Af2/kz7prG2b+IiJSjQCsizZu3LwRV0pPqV80Tu07Exx8CG3FeWC8rBEeCRb9iRUSagn7biojUUlpuCa/+vKPcsteX7OSfn/5JdqHzoQ7rk7P5bM0+V5RXzq870vlxU4qryxARaVSatktEBBjbsy1DurQh0M/KfZ+tr7bt77sz6BgewFOT+5Yte2mhM+CefVJ7To+P5Ks/9+Nj9WLGWU0788KxLj8tltTcYj78LYmxvaJOvIGIiJtSD62ICODva+WG07swtEubEzcG2oX5M6xb9TeV9e0YRnyU657yFRnsx+nxkS47vohIU1GgFRERERG31qKGHGRkZHDmmWeyZcsW8vKa7mlBIuICrTrDaTeBXwh4l3887uf24QxqCyf3v8pFxYmISFNqUYE2JCSE+fPnM2XKFFeXIiKNzccfzn6i0lV/mq580WUcJ3fp3cRF1U9aXjEv/LgdSz32YQFmL96Jt5eF9mEBJ2wvItIStKghBz4+PrRu3YhT8YiINKLVSVlsS8ll1iX96ryPxy46ifS8Yn7dkd5whYmINHMuD7S33XYbnTp1wmKxsHbt2nLrtm3bxrBhw4iPj2fQoEFs2LDBNUWKiDQ0/3DnZ9/yN41FhvozpIY3plXm1NhWxLR20WOARURcxOVDDiZPnsxdd93FiBEjKqybPn0606ZNY+rUqXzyySdMnTqVFStWsHHjRm6++eZybSdMmMA999xTo2POmjWLWbNmlb3Oyspi3rx59TuRWigqKqrz8ewOA8CihYs4OzeXEGDDhg34F6fhW5rDxiY8j4ZUn2vSUumalFfT63HwoB2A3Xt2M2/e3hO29c1PwQIczDXMmzePlAJTtv6nH38k0Kf8AIDNSc7nzWZlZjJv3jxK7Kbc+lWrVlK4y4tdu+yU2GHevORKj+0TdQN+rS6m0/6vKNi2nU1sBiC7hr+P1qZWfO7tokWLCPOzkJbqvAY7dmwn1dfCoXTjMT9Let9UpGtSka5JeS3herg80J5++umVLj906BArV67khx9+AODiiy/mlltuYfv27fTu3ZtFixbV+ZgzZsxgxowZZa+jo6MZP358nfdXW/Pmzavz8UrtDvj5O0afMZqQ5BAogD59+kDWXihII6YJz6Mh1eeatFS6JuXV9Hp8mbYaUg/QKa4T48dXP4b2q/TV9OgYhsUCRfuyGT++PztT8+C3nwEYM3YsYQE+5bbZu2Qn7NhEeKtWjB8/jMISOyz+vmz9gAEDOT0+ktXfbaKoxM748SdVX/Bnq6FNV3pZe8K2jYSFhzN+/PATnqdZfxDWryq3bPTo0bQN9eej/b9DRipdu3YjIsSPlM2HGD9+0An32RLofVORrklFuibltYTr4fIhB1XZu3cv7du3x9vbmbktFguxsbEkJVX/7PZx48axZs0axo0bx/r11U+O7i6MMazak8G+zAJXlyLS4hSVOtid3nDvra0puRhjTtxQREQajMt7aBvaggULXF1Cg1u8LY1r3vqdLhFBzPtb5T3aIlI3Ly3ajp+3F3dN6AlARIgf/WPDCfC1EuBjPeH2PlYLI7tHkFNko2tEEE/N20KH8NrPLtA3JpwuEUGM6Faz8bPxUcF0CIL+XduTklOEl8VC6HG9ySIinqLZBtqYmBgOHDiAzWbD29sbYwxJSUnExsa6urQmV1hiA6CgxO7iSkRanhKbgytPi+OqIXEAhPr78OnNJ/4v/yO8rV68f91pZa8P5hTV6b3aP7YVP/1jdI3bd4kM5qHB3owf37/WxxIRaWma7ZCDtm3b0r9/fz744AMA5s6dS3R0NN26dXNxZSIiIiLSnLg80E6fPp3o6Gj27dvH+PHjywXW2bNnM3v2bOLj43niiSd4++23G/TYiYmJTJkyhcLCwgbdb5Ozl0JhpqurEJGGkJUE6TugIMPVlYhIfRVmQvY+V1fhEVw+5GD27NlVruvRowfLli1rtGMnJCSQkJBAdHR0ox2jSSx8DIqyYfxMV1ciIvWxeym8MxG8vCGwNfxjq6srEpH6eH0MZOyCaQuhw6murqZFc3kPrdRcYamddcnZlazIhFF3w5Abm74oEalg3b4siksrzhN7Qvlp0LE/3LoK8lMbvjDg910ZrNit3l+RJpGfDhj9j0sTUKB1E7GtA+nRLoTJr/zq6lJEWoyxvdrSLyackfERDbbPs3pH8eUf+3l/+Z4G22dtndk7in4x4QzvVv68DuUWMWX2MhJeXUZqbrGLqhMRaXgKtG6iVaAPz17SD4emtxRpMBedGs3nfx3OGT3aNtg+pw7vzOgebcue6ucKV5wWx+d/Hc7gzq3LLT+2JlfWJyLS0BRoRURERMStKdCKiIiIiFtToBURERERt+byabtcKTExkcTERPefh1ZERETEg3l0D21CQgJz5swhIKD2z113BW8vCwAWC3h7efS3TqRKPlbn+8TH243eI5l7oKTA1VWIiLgtj+6hdTdRof58PG0IXl4WWgf5urockWbp3nN7cWbvdpzegFNxNarYIfDDg84HKXTo7+pqRETckgKtmzmtSxtXlyDSrLUN8efcvu1dXUbNDZgKmbuhONfVlYiIuC03+j85EREREZGKFGhFRERExK0p0IqINFepW2DHQkha7upKRKS+irKd7+eSfFdX0iIp0IqINEfGAbNPh6//Bu+eB7uWuLoiEamP+Q/B+xfCoidcXUmL5NGBNjExkSlTpmgeWhFpnmxFcN18aNMdSvV7SsStHXkP673cKDx6loOEhAQSEhKIjo52dSki4ibG9oriz33ZjO3Vtso2kwdEY3MYzu/XsQkrExHxXB4daEVEaqtzRBAvXHZqtW2Gd4tgeDc3mQdXRKQF8OghByIiIiLi/hRoRURczScI/pwD394JPoFVt1vzATzfz9lWRNzHklmw+Wuw+rm6khZLQw5ERFxt+G0Qe5rz64gezpvBKrPnV8jcBUnLoO+UpqtPROpnz1IYfgc4bGArdnU1LZICrYiIq3n7QefTj77O3OO6WkSkcUTEQ9oWV1fRYmnIgYiIiIi4NQVaEZEWLq/YxvZDeexOKyhbdii3imEN0iByikrZndYwT4RKySniUI6+XyLVUaAVEWnBukUGsz45m/P+s5S/vLOC3u1D6d0+lEkv/8rO1DxXl9diXf/OSkY/vYgfN6XUaz9pecWc/n8LGf30InKKShuoOpGWx6PH0CYmJpKYmKgnhYlI8/fTI3BoM/iF1mqzoV3bsOGRCRWWD3x0AblFtoaqTo6TUVDi/JxfUq/9FJbYKbY5ACgudYB/vUuTxnLgT3j/Iug8EhLeqbrdrsXwRBzEDYcDf0B4DPzl+yYrs6Xy6ECrJ4WJiNs4uA5G3QP2YijKdnU1InK89G1QkAbJq6pvd+TGsC3fOD/nHWzcujyEhhyIiLiL0A5g0a9tEZHj6TejiIiIiLg1BVoRERERcWsKtCIiIiLi1hRoRURERBrazkXOmzilSXj0LAciIs1ScBScPAWsPhAQ7upqRKS2SgvhvQuh57mwfy3k7HN1RS2eAq2ISHPj4w8Xv+7qKqSO5qzYS2queuY8mjGAgfP/A29VnAdaGp4CrYiISAP69zcbubBfRzbs13zBIk1FY2hFREQa2JVD4vD3sbq6DBGP4dE9tHr0rYiIiIj78+ge2oSEBObMmUNAQICrSxERERGROvLoQCsi4pZsJeCwu7oKEZFmw6OHHIiIuJ3wWFjyDPgGgXWsq6sREWkWFGhFRNxJ/2ugOBf2/AqtXV2MiEjzoCEHIiLuxGIBb39XVyEi0qwo0IqIiIiIW1OgFRERERG3pkDbzDkchh2p+a4uQ0RaoA37c1i5OwNjDKV2B7/vyiC7oLRs/fZDuWxLyWV/ViFr92a5rlA3V2yz8/uuDHKKjl7brSm5bD+UB8Ch3CJW7XF+H07kSNv8Yhu/78qgsESzXYiAbgpr9uau3scLP25j6rBOri5FRFqQEd3a8NLC7aTlFfPkxX05lFvE499u5ty+7Xnp8v5k5pdw9vNLMAYCfa3kFNn44LrTGNE9wtWlu53ZP+9k1vytXNw/mmemnEJKThETnluMt5cXK+4fx7T3VrF2bxYvXd6fc/u2r3ZfN76/itVJWUQE+5GWV8y007tw7zm9muhMRJovBdpmrqDEzsjukfxTv7BEpAE9d+mpAPzlnRXkFdvIK3b29OUX2wAotTsotTt7DHOKnMvyDq+T2skvcV63I9e2uNSBw0CJ3YHN7qDguPXVKTjcI5uWVwzoeyJyhAKtu3E44MtboSAdirLB4gXGAV76Vop4lOy9DDj4b7Aug3EP13w7Y5y/Q/JT4aJXG608EY9VWgif3gBYnP9GS5NQCnI3xg5rPzj6+pynwT8Mep7ruppEpOkdXEcEwB9ptQ+0a953fp2xsxEKE/Fw+amw5Tu45isICHd1NR5DgdbdhcdC/HhXVyEiIiJHeFmh80hXV+FRPDrQJiYmkpiYSGFhoatLEREREZE68ujBHQkJCcyZM4eAgABXlyIiIiIideTRgVZERERE3J8CrYiIiIi4NQVaEREREXFrCrQiIiIi4tYUaEVERETqym6DlI1QWlT3feQehMw9DVeTB/LoabtERERE6uXX5+HHR8AvrPL1HU51Pmwhsgd4+znbhcdAynrneocNXhwEJflw6ypo3bnpam9BFGhFRDzcmqQscopKATiYXcT2Q7n8uS+7xtsv2JhCaIAPgzu3bqwSWwxjDEu3p5W9/mbdAXIKbQCsTspk4intCfT1xu4wfLvuACk5R3v91iVnkV1Y2uQ1ywkUZjo/F1fxnpk0++jX0QPh5Mmw4TNInHp0eXGO83NJXqOU6AkUaEVEPNg5J7fnk1V7Abjn7J58sXY/5zy/FIcxXDY4BovFQmpuMVsO5la6/eaDOVz/3kq8LLDl0bPxsWokW3XW7M3i4S83cOWQWEptztDas30IV5wWy7vL9hDbJpCbR3fjx00p3DP3T06ODuOSgTF8vHIvf3lnJYM6tSIswIfNVXw/RDyVAq2IiAebPCCayQOiy153iwzm+vdWEhHsy8xJfcuWn/efpZVub7MbABymcetsKewOQ2SIH49eeHKFdbvS87Efvp52h6FbVAj/mzaUEpuDj1c6/+h46Lw+/N+8LQq0IsfRn9IiIiIi4tYUaEVERETErSnQioiIiIhbU6AVEREREbemQCsiIiIibk2BVkRERETcmgKtiIiIiLg1jw60iYmJTJkyhcLCQleXcmJ5qfD6GPj4SldXIiJNzSfg8OfA2m23fYHzkZrLX2n4mkTkKKvv4c9+rq3Dg3n0gxUSEhJISEggOjr6xI1dLXM3JK9ydRUi4gpT3oX07dDp9Nptl7Qc0rbCzkUw5KZGKU1EgFOvgl4ToTALPrnW1dV4JI8OtCIibiEs2vkhIs2Tlzd0HQN7fnV1JR7Lo4cciIiIiIj7U6AVEREREbemQCsiIiIibk2BVkRERETcmgKtiIiIiLg1zXIgIiI18sOGgwzr1oYSm4OPV+xlVHykq0tqMNsP5TJvQwoXntqR1oG+vLdsNyd1DGN4twgAFmxMYUtKLlYvC1cPjSO7sJRPVydzZu8o4qNCqtzvjtR81uzJAqDIZuerP/ZXW0dKbhEvLdxOic1Rq/rtDsMHy/eQV2yjbYgfESF+7DiUx1VD42q1HxF3pUArIiJlBnZqxWWDY+hxXEi7fmRnZn67mS/WJJORX8qzC7by264M7hrfw0WVNqznf9zOV3/sJz2vhFNiwpj53WZiWwey+K4zALjj47UM6dKatXuziAz2Y0tKLq8t3smG/dm8fMWAKvf76s876N0+lAv6deCLtftpFejDP6q5Zh8sTyr7+pSY8BrX/+e+LB7/dhNnn9SOp+ZtKVveqU1Qjfch4s405EBERMqEB/oyc1Jfpg7vXG75Bf06clLHUBdV1TzcPaEn3doG13q7c/u255TocAA6RQRxxWmN02saHujD0wmnNMq+RZo7BVoRERERcWsKtCIiIiLi1hRoRURERMStKdCKiLgpm5c/5KfCqyNcXYqIAPiHg8ULAiOqbxfYxvk5NBq8A8A3GCzWRi+vJdMsByIibio/oCNhl78Or49xdSkiAhDVG+5YDz4B1bfrfDrc/icEhENpEVgs8OKgJimxpVKgFRFxZ0FtXV2BiBwrrGPN2rU6PNuFf1jj1eJBNORARERERNyaAq2IiIiIuDUFWhERERFxawq0IiIiIuLWFGhFRERExK0p0IqIiIiIW1OgFRERERG3pkArIiIiIm7Nox+skJiYSGJiIoWFha4uRURERETqyKN7aBMSEpgzZw4BASd4RJ2IiIiINFseHWhFRERExP0p0IqIiIiIW1OgFRERERG3pkArIiIiIm5NgVZEpCXIT4eiHFdXUXu2EijIcHUVIlUrzHS+v0qPmRHJVuz8uXXYyy+v97Gy9H6oIwVaERF3Ex4HQIF/FPgFQ2AbeKoLPB0PmXtcXFwtfXo9/F9n2PiFqysRqWj3Uniys/P99crwo8s/nOL8uX1jLKx4A1p3rv+xWneGdyc697t9Qf3352EUaJuxolI7O1LzXF2GiDQ38WfBfSn82f02CGgFf98C96WAb6CzN6kRbT+UR4nd3nA7zE52fs7Z33D7bGBbDuZiczjKXm9NyWVfZgEAabklLNmWyr7MAtYnZ7NkWyo2u2my2g5kFZKZX9Jkx/M4uQchZjDc9CvkJB9dfuTndv8aOOM+OO3G+h/rugXO93Hn0yHnQP3352E8+sEKzd3ri3fy2Zpk/n5mPJDm6nJEpDnx8QfL4T4Jq4/zA0ujHnJY1wieXbCVYpvjxI1biKJSO+f9Zyl9OobSNtSfkd0j+XT1PgBuGNmZhVtSuWfuOnIKS8kvsdE5IojeHUJpF+rPiO4RpOQU0T+2FX4+XnRrG8zp3SOrPNbQLm34Y28WZ/Roy89bUxnVPQIAby8LY3u2JbOghI7hR+dNPzU2nHXJOTzwxXquG9EAPYRSOYsXWP2qXu/lDZYGeO9ZvZ0fFmv99+WBFGibscJSO+ef0oGpwzvDXgVaEXGtv4zozPbUPD78LcnVpTQZu8NQYnfwzrWDCQvw4a9ndOOvZ3QrW3/fufDbznQueW05vlYvfvz76LJ1N4/uxs2jj7ZdMGNUtcdKGBhDwsAYAO4/ZrmXl4U3pw6q0H50fFtaBfmweGtq3U5OpAXRkAMRERERcWsKtCIiIiLi1hRoRURERMStKdCKiIiIiFtToBURERERt6ZAKyIiIiJuTYFWRERERNyaAq2IiIiIuDUFWhERERFxawq0IiIiIuLWFGhFRERExK0p0IqIiIiIW1OgFRERERG3pkArIiIiIm5NgVZERERE3JoCrYiIiIi4NQVaEREREXFrCrQiIiIi4tYUaEVERETErSnQioiIiIhbU6AVEREREbfm7eoCpHox+evh+SuhtBD8w6E4Fyxe4BMIxdngG+TqEkWkOVr9Pvz2Gnj7137bvFT44CIIjICrPgOLpdzq073+4DHvt1iWfR7wcIOUK1IvB9fBnKuhyxkwcdYJm/sXp8FLQ6BVHJTkQ9Ye8AmCjgNg9xK44EXofPrRDXz8wV4Kb58DfqGQuQu8A8BW2HjntPlb+P5u5znt+QViToMLX26847k5BdpmrmPhFghuB+MegvC4w4HW4gy0+Yecbz4RkePtXQ69JkLbXrB7ae22zdnnDAgAxgEWa7nV/Sw7iPFKJbP4zwYqVqSeDq6HjJ3ODp8aCCw6CKmbnB8AV8yFL2+FtR84XyevLh9ow6Lh+vnw+hjn6ynvwe+vO8NvY9m3ArKSYPW7zteljRieWwAFWncQ2BrihlVcHh7T9LWIiPsIiwGfAFdXIdL8xQw+8XslokflX0uzoDG0IiIiIuLWFGhFRERExK0p0IqIiIiIW1OgFRERERG31qIC7dKlSxkyZAjDhg3jmWeecXU5IiIiItIEWlSg7dKlC4sXL+bXX3/l66+/pqCgwNUliYiIiEgja1HTdnXo0KHsa6vVipdXi8rrIiIiIlIJlye+2267jU6dOmGxWFi7dm25ddu2bWPYsGHEx8czaNAgNmzYUKN9zp8/n65du+LvX4cn5IiIiIiIW3F5D+3kyZO56667GDFiRIV106dPZ9q0aUydOpVPPvmEqVOnsmLFCjZu3MjNN99cru2ECRO455572LdvHzNnzuTLL7+s8pizZs1i1qyjj8bLyspi3rx5DXdSJ1BUVFSj4+3caaddXiaHCg6xpgnrc4WaXhNPomtSnq5HRcdfk9ElJaxatozc4BT67EumyK+IEp80IrKO+R1iHIw/3H7Z8uXkBKdV2G9o3k6GHv563g8/lHv60r69diIOf11SUsKaZcvK1s3/4QesXuUfk3sip2VnEw5s2ryZpOz6f3/r+nNy8IAdgD17dmPJdJ5DQUEBCxYsAOCnH38k0Kfyc9uaZQBwOBxN8jOaluasdfv27QT5wKEMw2/L0ykusvPDDz+Ua7t6zWp6BJe0+PdOh9R1nAzk5+eztAbnGlxSUu71gh9/ZGhBAUceJr9l61Z258+jXdofxGZm8vu8eVjtRYw7vH7pL7/QKyODNsDWbdvYVdBw13dAejoHN6wnsOggXY5ZXlhUxOJG+j62hN+vLg+0p59+eqXLDx06xMqVK8venBdffDG33HIL27dvp3fv3ixatKjCNsXFxUydOpVXXnmF4ODgKo85Y8YMZsyYUfY6Ojqa8ePHV9m+oc2bN69Gx/vj+8202tuKtsFtm7Q+V6jpNfEkuibl6XpUVOGa/OHLsKFDoUM/KPoSQqMhOBK27TvazuGA5c4vhw4ZUvnjs/evgcNPvh1/1lngdfTRtz8XrIMU59e+vr4MHToUVjofrXvmWWfhY63lf/wl/R/kQa+ePek1pP7f37r+nHydsQYO7ScurhN9Y8Jg41oCAwMZN24kLJnHmLFjCQvwqXTb8J3psGY5Xl5eTfIz+t/k3yEjlW7dutEqyIe0ramcNqQbb29fxVlnjYGfvytr2//U/tj3rm357521GbAdgoKCanSuK+asL/d63NixsCMQipyve8TH02PEeFiXC8UrnPsszoPfnetHDB8O2Z9BDsR37078yAa8vimvENHnJMgIhOSjiwP8/Rvt+9gSfr+6fMhBVfbu3Uv79u3x9nZmbovFQmxsLElJSVVu8+GHH7Jx40amT5/O6NGjSU5OrrKtiIiIiLQMLu+hbUjXXnst1157ravLEBEREZEm1Gx7aGNiYjhw4AA2mw0AYwxJSUnExsa6uDIRERERaU6abaBt27Yt/fv354MPPgBg7ty5REdH061bNxdXJiIiIiLNicsD7fTp04mOjmbfPudNC8cG1tmzZzN79mzi4+N54oknePvttxv02ImJiUyZMoXCwsIG3a+IiIiINB2Xj6GdPXt2let69OjBsmOmhGloCQkJJCQkEB0d3WjHEBEREZHG5fJAK5UrKrWTnldy4oYiIi5idxiS0vPLXqfmFpNTVIqP1YsOYQHsycina2QwO1LzytpEBvvRJtiPQ7lFFJbY6WgM3jjvkzh2ltddafkU2+y0CvQlKtT5kJzcolKSs5z/o3bsftuHBpBTVEp+iY30IkNWQQlpeSV0a+ucvtEYw9aUPDq2CiDYz/nP3sHsIrIKS/D3thIR4kdOYamzLYYD2UWNdckaRW6RjT3p9XvUe3ZhKSk5RXRvG4zFUvl8u4dyiygqcRDbJrBexxJpDAq0zdRT87bw2dpkEvr6g93V1YiIOPXpEEqWj3O0WkGJjXs/XcfJHcNYl5z9/+3de1SU5b4H8O8wIIxyEQVBAUHj0uY2GFdpo6kJlW67snV3wV3tVerZq3vbtBRrldbOI1ktdbl24U6PbkSNTlrnpHmhk5ZYsvGagJAgiIiMINcZ+J0/JkZwABWV8Z35ftaaxcz7vM/MM1/GZ34+8zIvUj7IhYhx+4jBGpTXNsFviAZnLjRD46BGW7vAw8URe16dgPuWf4eahlZ86VSHcAAFpy9A+9tj5Jfp8PDKvRjooIYA+GnB3XC0V+OFf+Vjb3EN2toFw1wdUVXXDAe1HVydHFDT0AKNgxqNLW344HAuzta34J9PxWF8sCey8srw2pZDSA71wuq0GDS1tmHSf+6GnZ0Kja1t8HZ1Qm1jK4KGOWPTgXI06dsQOtwVF1sMV5WJj7sGw92c4Dekfwq9CB9X5J+qxe+Gu8BV44BlVSew8IvDSBg9FHYqFbS+bqhpaEWz/urfPOb810/4vqgGH/1pDP6gHdHtPskZuahr0uObF8chcJjLjXo6RDeExY+hpe7pGvX464RAxPgPsfRQiIhMHov3x3/cZfxbhztv80DBohR8+udYAMaVwv95IQkRPm4orzWupJadb8I0rQ8KFqUge1YidI3GlVBdox4igL7NeJatptZLxVddkx6jPAYh928TcLHFgPZ243Zdkx5/fyQSj8aPRHltE+6LGI7VT8TgTF0zHO3VKFiUApUKOFvf8ttjtJr6df5paG9HQ2sb9r42EX7uGpzWNWHxgxEYH+yJ+hYDUsK9sWBq6FVn4us+EPvmTcLGZ8deeecb4NWU21GwKAXJYd5IGD0U+QuTUbAoBavTYmBnp8IXf/09/m/uRHi6XP3p302/l98y6mmfdjH+noluNSxoiYiIiEjRWNASERERkaKxoCUiIiIiRbPpPwrLzs5GdnY2v4eWiIiISMFseoU2NTUVGzduhEajsfRQiIiIiKiPbLqgJSIiIiLlY0FLRERERIrGgpaIiIiIFI0FLREREREpGgtaIiIiIlI0FrREREREpGj8Hlp+Dy0RERGRotn0Ci2/h5aIiIhI+Wy6oCUiIiIi5WNBS0RERESKxoKWiIiIiBSNBS0RERERKRoLWiIiIiJSNBa0RERERKRoLGiJiIiISNFY0BIRERGRovFMYRY4U5hLQwnwSTJw+xTgzue73SeybheSyzYBnh6AA0/8QERXQT0AyJkNDBgEnC8BEmYbt53aB3y3DEh6qee+pd8DO9KN1128L23PmQ00VANe4UDZj8CUZeZ9DS1Y4fAB1GjH4N078I5uP761D0KS3SG8b5gOwNesy2L1anirz8Ed9V0bqk9Au302nmgJAtpi8LHDcgzIXgsM8cfi87vhuccJFcOeA+B0zfGYtBvwocNH0GSvgUae7NIUqSrGy6fegcr1QQAxfX8MAPhxNXBoI5CyGPCL636fr/4GnD0K+MUDJXuA5HeAkfG932/eJ8C/Nxiv//5F43tJD3yK/4Xhv6wFNAXAif8FxjwORP/Z2Chi/P1ePAvn9r907Vh1FNj6IhB0NzDu1Z7H8us+YPsCQPsnIPZpwNACbP4LIO2Aqw9QdRh4YCXg7n+pT10FsOUZYGgg0HQeaNMDD38CDBhofv91lcDnzwD6Tu/TAwYBD/0DcPYETnwD5P4dcHK71N6mN96/vgl4+B/Az/8EjnxubAtKAQq/we9qTgP2ToChGVDZGS/qAcZ9VFe51texf8dPsiibLmhTU1ORmpoKX1/zyfZmcq87ZnxjaG/rsaANrd+H4fWHgHoAIff16/iISKEe3wScO3HpdlAKoHYAaoqBozm9F7Qle4xv8D53AN8vv7S9IMv4s3in8eepfWZdVS0XcJ96v/HGvw8gHEC4/WEAwO/tDuEkUsz6PGiXCwdVm/k4yvPgVn0Ad9lVQdVaj6nqH4FCY9PBQY8jRQrgX58PIKHn53Il+iZMU+8DigFPzTQcwxBTU7zdMYxuPoK6Uxpcd0F7/EugPA8oye25oC3IApp1QOl3xtsluVcuaI9vM/4Ho6EaKNzea0HreXoHBl8sBHa+bdygGXKpoAVMhfFw1ykAOv1HpuxHoOwHoLWh94K2JNf4HB1djQVt8wXg2H933acyv2tBW3XU+Hw7njMAXKwChowyv/+zR4FzRcA9iy9t2/aK8XXu7AkUf2t8/M5aG4AjW4zX688Ax74ERtwB1JYAu94G/OJR5PdHRE1+FKivNC4aOToDf/wMOPcLcGhTz8+3sz98AJz+CQi+9+r2p5vKpgtaIiKr4hVmvFzOLw44uevK/d0DgJFjuxa0t5DDA7QYpzlr6WHcGkaMAXS/Ao3nLT2Sm8/RBQh78NLt7Quv/T58YwE7NVD4DeDmiyqXsYBnsPHSoeN2x2rulQweabzQLYHH0BIRERGRorGgJSIiIiJFY0FLRERERIrGgpaIiIiIFI0FLREREREpGgtaIiIiIlI0FrREREREpGgsaImIiIhI0Wz6xAqWOvUtEREREd04Nr1Cm5qaio0bN0Kj0Vh6KERERETURzZd0BIRERGR8rGgJSIiIiJFY0FLRERERIrGgpaIiIiIFI0FLREREREpGgtaIiIiIlI0FrREREREpGgsaImIiIhI0VjQEhEREZGisaAlIiIiIkVjQUtEREREisaCloiIiIgUzd7SA7Ck7OxsZGdno6mpydJDISIiIqI+sukV2tTUVGzcuBEajcbSQyEiIiKiPrLpgpaIiIiIlI8FLREREREpmkpExNKDsDRHR0d4enr22+NdvHgRzs7O/fZ4SsBMzDGTrpiHOWZijpmYYybmmElXSsmjuroaLS0t3baxoLUAX19flJeXW3oYtxRmYo6ZdMU8zDETc8zEHDMxx0y6soY8eMgBERERESkaC1oiIiIiUjQWtBbw0ksvWXoItxxmYo6ZdMU8zDETc8zEHDMxx0y6soY8eAwtERERESkaV2iJiIiISNFY0BIRERGRorGg7UeFhYVITExEcHAwYmNjceTIEUsPqV8EBAQgJCQEUVFRiIqKQlZWFoDe87C2rJ577jkEBARApVIhPz/ftL2vGVhDPj1l0tPrBbDuTJqbm/HAAw8gODgYWq0WkydPRlFREQDg7NmzuOeeexAUFITw8HDk5uaa+vW1TQl6y+Suu+7CqFGjTK+TjIwMUz9rzgQAkpOTERkZiaioKCQlJeHgwYMAbHc+6SkPW51LOsvMzIRKpUJOTg4AK59LhPrNhAkTJDMzU0REsrOzJSYmxrID6if+/v5y8OBBs+295WFtWe3Zs0fKysrMsuhrBtaQT0+Z9PR6EbHuTJqammTbtm3S3t4uIiIfffSRjB8/XkREnnzySUlPTxcRkf3794uPj4+0trZeV5sS9JbJ+PHj5fPPP++2nzVnIiJSW1trur5lyxaJjIwUEdudT3rKw1bnkg4lJSUyduxYSUhIMP1bsea5hAVtP6mqqhIXFxfR6/UiItLe3i5eXl5SWFho4ZHdfN1NKr3lYc1Zdc6irxlYWz5XW9DaUiYiInl5eeLv7y8iIoMGDZLKykpTW2xsrGzfvv262pSocya9FbS2lElmZqZotVrOJ7/pyEPEtueStrY2mTRpkhw4cKDLvxVrnkt4yEE/KSsrw/Dhw2Fvbw8AUKlUGDlyJE6dOmXhkfWPtLQ0RERE4Omnn0Z1dXWvedhKVn3NwBbyufz1AvQ9L6Vavnw57r//ftTU1ECv18Pb29vUFhAQgFOnTvW5Tak6Munw2muvISIiAtOnT8fJkycBwGYySUtLg5+fHxYsWIC1a9fa/HxyeR6dt9viXLJs2TLceeediI6ONm2z9rmEBS3ddLm5uSgoKMDPP/8MDw8PzJw509JDolsYXy/A4sWLUVRUhCVLllh6KLeMyzNZu3Ytjh8/joKCAiQlJWHq1KkWHmH/+uyzz1BWVoa3334bc+fOtfRwLK67PGx1Ljl8+DA2b96MN954w9JD6V+WXiK2FdbyMcb1qqioEGdnZ5v4yKc7POTAXG/HuXW8XkRs42NCEZH3339foqOjuxwXOHDgwB4/7utrm5J0l8nlHB0d5dy5cyJiG5l05uTkJGfOnOF88hsnJyfTa6GDLc0lK1asEG9vb/H39xd/f39xdHQUT09PWbFihVXPJVyh7SfDhg3DHXfcgXXr1gEANm/eDF9fXwQGBlp4ZDdXQ0MDdDqd6faGDRswZsyYXvOwlaz6moE159PT6wXoe15KsmzZMmzYsAHbt2/H4MGDTdtTU1OxatUqAEBeXh5Onz6N8ePHX1ebUnSXicFgQFVVlWmfzZs3w8vLC0OHDgVg3ZnodDpUVFSYbufk5GDo0KE2O5/0lIeTk5PNziWzZ89GZWUlSktLUVpaioSEBKxevRqzZ8+27rnE0hW1LTl+/LgkJCRIUFCQREdHS0FBgaWHdNMVFxdLVFSURERESHh4uEybNk1KSkpEpPc8rC2rZ555Rnx8fEStVsuwYcPktttuE5G+Z2AN+XSXSW+vFxHrzqSsrEwAyOjRo0Wr1YpWq5W4uDgRETlz5oxMnjxZAgMDJTQ0VHbu3Gnq19c2Jegpk4sXL0p0dLSEh4dLZGSkTJw4UfLz8039rDmT0tJSiY2NNT33SZMmmT7hsMX5pKc8bHkuuVznPwqz5rmEp74lIiIiIkXjIQdEREREpGgsaImIiIhI0VjQEhEREZGisaAlIiIiIkVjQUtEREREisaCloiIiIgUjQUtEdFVCggIQEhICKKiokyXQ4cOWXpYV2337t2Iioqy9DAAAKWlpV1OHkFEdD3sLT0AIiIlycrKumlFocFggL09p2UiomvFFVoiohtApVJh8eLFiIuLw6hRo5CZmWlqKywsxJQpUxAbG4vIyEh8/PHHXfqlp6cjNjYW8+bNQ2VlJZKTkxEaGork5GTMmDEDixYtQnNzM7y9vVFWVmbqO3/+fMydO7fb8bz33nuIiIiAVqtFQkICGhsbARiL5jlz5kCr1SIsLAwHDhwwbU9JSUFMTAzCwsLw6KOPoqGhAYBxZTc8PLzbfh0rrenp6YiOjkZgYCC++uor0zjy8vIwceJExMTEYMyYMcjOzr5BiRMRdWLpU5URESmFv7+/BAcHm07DqtVqpbGxUUREAMjSpUtFROTYsWPi7Owser1eDAaDREdHy7Fjx0REpKGhQSIiImT//v2mfm+++abpMR555BFZuHChiIhUVlaKl5eXpKeni4jI/PnzZd68eSIi0tzcLN7e3lJaWmo2zjVr1khsbKzodDoRETl//rwYDAbZtWuXqNVq+eGHH0REZOXKlZKcnCwiIu3t7XLu3DnT9VmzZsmSJUtERHrtV1JSIgBk06ZNIiLy9ddfS3BwsIiI1NbWSlRUlFRUVIiISHV1tfj5+Ul5ebmUlJSIm5tbH38TRERd8bMtIqJr0NshB4899hgA4Pbbb4e9vT3OnDmDuro6HDlyBDNmzDDtV19fj6NHjyI2NhYA8NRTT5navv32WyxduhQA4O3tjalTp5ra5syZg7i4OKSnpyM7OxtxcXHw9/c3G8fWrVsxa9YsuLm5AQDc3d1NbYGBgYiPjwcAjB071vRYIoKMjAxs27YNBoMBFy5cQGJi4hX7AYCTkxMeeughU1txcTEAYO/evTh58iTuvffeLuP75ZdfMHr06G4zJCLqCxa0REQ3iJOTk+m6Wq2GwWCAiGDIkCHIz8/vsZ+zs3OPbSqVynTdx8cH48aNQ1ZWFlauXIm33nrrhowRANavX4+dO3diz549cHV1xYcffoidO3desR8AODo6msapVqvR1tYGwFgkh4WFYe/evWbjKC0tveaxExH1hMfQEhHdRCEhIXB1de1yTG1RURHOnz/f7f4TJ07EmjVrAABVVVXYunVrl/bnn38er7/+OnQ6He6+++5u72PatGlYtWoVLly4AADQ6XSmIrMntbW18PDwgKurK+rr601juB6JiYkoKSnBjh07TNvy8/PR2tp63fdNRNQZV2iJiK7B9OnTodFoTLczMjIwYcKEHve3t7fH1q1b8cILLyAjIwNtbW3w8PDA+vXru91/+fLlmDlzJkJDQzFixAjEx8d3+XqrhIQEuLm54dlnn+2yetvZE088gYqKCiQmJsLe3h6DBg3qUlR2Jy0tDV988QVCQkLg6emJpKQk/Prrr732uRJ3d3ds27YNr7zyCl5++WXo9XqMHDkSOTk513W/RESXU4mIWHoQRERk1NTUBAcHB9jb26OmpgYJCQlYt26d6fjV06dPIyYmBidOnICLi4uFR0tEdGvgCi0R0S2ksLAQaWlpEBG0trZizpw5pmJ24cKF+PTTT/Huu++ymCUi6oQrtERERESkaPyjMCIiIiJSNBa0RERERKRoLGiJiIiISNFY0BIRERGRorGgJSIiIiJFY0FLRERERIr2/00+2EMHZzNfAAAAAElFTkSuQmCC" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for idx, (spct1, spct2) in enumerate(zip(summed_spectra, single_ch_spectra)):\n", - " plt.figure(figsize=(10,10), dpi=80)\n", - " plt.hist(spct2, N_BINS, range=(0,BITS_12), weights=(1/times_ch1_4[idx])*np.ones_like(spct2), log=True, histtype='step', label='SiPM-Ch.1-4')\n", - " plt.hist(spct1, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(spct1), log=True, histtype='step', label='SiPM-Ch.5-8')\n", - " plt.legend()\n", - " plt.xlabel(r'Energy channel')\n", - " plt.ylabel(r'Count rate ($s^{-1}$)')\n", - " plt.title('Current comparator background spectra, SIPHRA-Channel '+legend[idx])\n", - " plt.xticks(np.arange(0,4500,500))\n", - " plt.grid()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2026-02-06T13:27:11.972817300Z", - "start_time": "2026-02-06T13:27:11.964869500Z" - } - }, - "id": "5d4f19c5bbfd41eb", - "execution_count": 8 - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/260206_CMIS_voffset.ipynb b/noteboks/260206_CMIS_voffset.ipynb deleted file mode 100644 index 0ccf543..0000000 --- a/noteboks/260206_CMIS_voffset.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T10:44:04.423897Z", - "start_time": "2026-02-06T10:44:04.420846Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f22322a36b757cda", - "metadata": {}, - "source": [ - "# CMIS input voltage offset comparison\n", - "\n", - "We acquired spectra from SIPHRA channel 2 using QC, feeding from SiPM channels 5-8 and compared the output for different values of the `cmis_detector_voffset` parameter (Range: 0 - 255). This parameter controls the voltage accross Vin (see schematic below), which is used to downscalethe input current. The CMIS input offset voltage modifies the SiPM bias voltage, allowing for gain control. The effect of modifying this parameter is clearly seen in the spectra below. Notice that higher values of `cmis_detector_voffset` correspond to higher SiPM gain (i.e. lower Vin voltage), which is clear from the decrease in dynamic range and increase in events triggered from noise (lower energy peak).\n", - "\n", - "**We might want to keep this parameter at 0 to maximize dynamic range.**\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "75bebada911337a", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T15:07:25.679855Z", - "start_time": "2026-02-06T15:07:25.151406Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "dfs = []\n", - "dfs.append(pd.read_csv('../data/260206/1_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset0_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260206/4_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset50_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260206/2_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset127_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260206/3_SiPM_ChannelsTest_Ch5-8_Ch2_QT_Thr20_cmisvoffset255_Background.csv'))\n", - "\n", - "# dfs[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "cb4c1aad-40db-456b-a316-8ce713563841", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T15:07:26.798447Z", - "start_time": "2026-02-06T15:07:26.789831Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "BITS_12 = 2**12\n", - "N_BINS = 512\n", - "\n", - "summed_spectra = [df['Ch2'].tolist() for df in dfs]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "436876e4-6c49-4cc6-9552-6551a7059e27", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-06T15:07:28.977212Z", - "start_time": "2026-02-06T15:07:28.227680Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "jetTransient": { - "display_id": null - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [286.980, 179.172, 161.187, 97.309]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['0', '50', '127', '255']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend(title=\"cmis_detector_voffset\")\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('QC background spectra, SIPHRA-Ch.2 SiPM-Chs.5-8')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/260209_Cs137_BGsubtraction.ipynb b/noteboks/260209_Cs137_BGsubtraction.ipynb deleted file mode 100644 index 8a9508c..0000000 --- a/noteboks/260209_Cs137_BGsubtraction.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-09T14:07:23.324172Z", - "start_time": "2026-02-09T14:07:23.320337Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "75bebada911337a", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-09T14:02:27.067251Z", - "start_time": "2026-02-09T14:02:24.867078Z" - } - }, - "outputs": [], - "source": [ - "BITS_12 = 2**12\n", - "N_BINS = 512\n", - "\n", - "dfs_SiPM58_BG = []\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/1_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "times_SiPM58_BG = [119.634, 161.062, 148.920, 164.828]\n", - "\n", - "dfs_SiPM14_BG = []\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "times_SiPM14_BG = [34.836, 80.792, 54.386, 90.837]\n", - "\n", - "dfs_SiPM14_Cs137 = []\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/12_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/11_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/10_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/9_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "times_SiPM14_Cs137 = [23.381, 24.606, 24.144, 24.645]\n", - "\n", - "dfs_SiPM58_Cs137 = []\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/13_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/14_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/15_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/16_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "times_SiPM58_Cs137 = [24.720, 24.916, 24.848, 24.934]\n", - "\n", - "SIPHRA_Ch_list = ['Ch2', 'Ch6', 'Ch10', 'Ch14']\n", - "dfs = [dfs_SiPM14_BG, dfs_SiPM14_Cs137, dfs_SiPM58_BG, dfs_SiPM58_Cs137]\n", - "data_SiPM_chs = ['1-4', '1-4', '5-8', '5-8']\n", - "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "436876e4-6c49-4cc6-9552-6551a7059e27", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-09T14:04:20.965711Z", - "start_time": "2026-02-09T14:04:20.634259Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "for idx,(data, ch) in enumerate(zip(dfs_SiPM14_Cs137, SIPHRA_Ch_list)):\n", - " plt.hist(data[ch], N_BINS, range=(0,BITS_12), weights=(1/times_SiPM14_Cs137[idx])*np.ones_like(data[ch]), log=True, histtype='step', label=legend[idx])\n", - "plt.legend(title=\"SIPHRA active channel\")\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title(r'$^{137}$Cs spectra, CC, SiPM Ch.1-4')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8127dbeb-d8f3-44bc-abd2-4a164c19731d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10), dpi=80)\n", - "for idx,(data, ch) in enumerate(zip(dfs_SiPM58_Cs137, SIPHRA_Ch_list)):\n", - " plt.hist(data[ch], N_BINS, range=(0,BITS_12), weights=(1/times_SiPM58_Cs137[idx])*np.ones_like(data[ch]), log=True, histtype='step', label=legend[idx])\n", - "plt.legend(title=\"SIPHRA active channel\")\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title(r'$^{137}$Cs spectra, CC, SiPM Ch.5-8')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "926fc8c293cea7a4", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-09T14:04:23.603853Z", - "start_time": "2026-02-09T14:04:23.361384Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "jetTransient": { - "display_id": null - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10), dpi=80)\n", - "for idx,(data, ch) in enumerate(zip(dfs_SiPM14_BG, SIPHRA_Ch_list)):\n", - " plt.hist(data[ch], N_BINS, range=(0,BITS_12), weights=(1/times_SiPM14_BG[idx])*np.ones_like(data[ch]), log=True, histtype='step', label=legend[idx])\n", - "plt.legend(title=\"SIPHRA active channel\")\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Background spectra, CC, SiPM Ch.1-4')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "710025b1f4f33391", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-09T14:21:34.842292Z", - "start_time": "2026-02-09T14:21:34.835721Z" - } - }, - "outputs": [], - "source": [ - "H_bg, bins_bg = np.histogram(dfs_SiPM14_BG[0]['Ch2'], bins=N_BINS, range=(0,BITS_12))\n", - "H_cs, bins_cs = np.histogram(dfs_SiPM14_Cs137[0]['Ch2'], bins=N_BINS, range=(0,BITS_12))\n", - "H_cs_subt = (H_cs/times_SiPM14_Cs137[0] - H_bg/times_SiPM14_BG[0]).clip(min=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "233314b3cd8deb29", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-09T14:39:03.778602Z", - "start_time": "2026-02-09T14:39:03.180322Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "jetTransient": { - "display_id": null - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10), dpi=80)\n", - "plt.bar(bins_cs[:-1], H_cs_subt, width=BITS_12/N_BINS, log=True)\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "plt.show()" - ] - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/260212.ipynb b/noteboks/260212.ipynb deleted file mode 100644 index b058d9a..0000000 --- a/noteboks/260212.ipynb +++ /dev/null @@ -1,214 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "id": "cf4cc785-eca2-4680-893e-a7ba5bf4b69e", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-13T09:12:20.943604900Z", - "start_time": "2026-02-13T09:12:20.924522400Z" - } - }, - "source": [ - "import sys\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ], - "outputs": [], - "execution_count": 38 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "# Testing the new channels\n", - "\n", - "We recently got 4 new SiPHRA channels, so now we have connection to\n", - "channels 2, 4, 6, 8, 10, 12, 14, and 16. We connected each channel to SiPM\n", - "ch 5-8 and tested them individually with only background radiation. Then\n", - "we did the same test, but with a Cs-137 source. \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "
Parameter
Value
Charge comparator threshold (active channel)
20
Charge comparator threshold (inactive channels)
255
" - ], - "id": "f22322a36b757cda" - }, - { - "cell_type": "code", - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "BITS_12 = 2**12\n", - "N_BINS = 512 " - ], - "metadata": { - "collapsed": false - }, - "id": "2dd4b1b5058ccc07", - "execution_count": null - }, - { - "cell_type": "code", - "outputs": [], - "source": [ - "#Importing background data\n", - "dfs_bg = []\n", - "dfs_bg.append(pd.read_csv('../data/260212/1_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch2_QT_thr20_background.csv')) \n", - "dfs_bg.append(pd.read_csv('../data/260212/5_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch4_QT_thr20_background.csv'))\n", - "dfs_bg.append(pd.read_csv('../data/260212/2_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch6_QT_thr20_background.csv'))\n", - "dfs_bg.append(pd.read_csv('../data/260212/6_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch8_QT_thr20_background.csv'))\n", - "dfs_bg.append(pd.read_csv('../data/260212/3_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch10_QT_thr20_background.csv'))\n", - "dfs_bg.append(pd.read_csv('../data/260212/7_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch12_QT_thr20_background.csv'))\n", - "dfs_bg.append(pd.read_csv('../data/260212/4_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch14_QT_thr20_background.csv'))\n", - "dfs_bg.append(pd.read_csv('../data/260212/8_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch16_QT_thr20_background.csv'))\n", - "\n", - "background_times = [158.063, 139.765, 174.846, 128.852, 164.299, 180.618, 132.460, 156.538]\n", - "\n", - "single_ch_spectra_bg = [df[ch].tolist() for df, ch in zip(dfs_bg, ['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16'])]" - ], - "metadata": { - "collapsed": false - }, - "id": "75bebada911337a", - "execution_count": 39 - }, - { - "cell_type": "code", - "outputs": [ - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Plotting background spectra\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.2', 'Ch.4', 'Ch.6', 'Ch.8', 'Ch.10', 'Ch.12', 'Ch.14', 'Ch.16']\n", - "for idx,s in enumerate(single_ch_spectra_bg):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/background_times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Charge comparator background spectra, SiPM-Channels 5-8')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "#plt.savefig('../figures/QC_SiPMch5-8_8channels_bg.png')\n", - "plt.show()" - ], - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-13T09:33:47.318900500Z", - "start_time": "2026-02-13T09:33:46.027690500Z" - } - }, - "id": "cb4c1aad-40db-456b-a316-8ce713563841", - "execution_count": 44 - }, - { - "cell_type": "code", - "id": "e68ab9ee-bdeb-4c34-b3ef-e032a4b3a6ec", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-13T09:12:32.032627Z", - "start_time": "2026-02-13T09:12:28.650229500Z" - } - }, - "source": [ - "#Importing 137Cs data\n", - "dfs_Cs = []\n", - "dfs_Cs.append(pd.read_csv('../data/260212/9_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch2_QT_thr20_Cs137.csv')) \n", - "dfs_Cs.append(pd.read_csv('../data/260212/10_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch4_QT_thr20_Cs137.csv'))\n", - "dfs_Cs.append(pd.read_csv('../data/260212/11_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch6_QT_thr20_Cs137.csv'))\n", - "dfs_Cs.append(pd.read_csv('../data/260212/12_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch8_QT_thr20_Cs137.csv'))\n", - "dfs_Cs.append(pd.read_csv('../data/260212/13_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch10_QT_thr20_Cs137.csv'))\n", - "dfs_Cs.append(pd.read_csv('../data/260212/14_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch12_QT_thr20_Cs137.csv'))\n", - "dfs_Cs.append(pd.read_csv('../data/260212/15_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch14_QT_thr20_Cs137.csv'))\n", - "dfs_Cs.append(pd.read_csv('../data/260212/16_SiPHRA_SiPM_newChannelsTest_Ch5-8_Ch16_QT_thr20_Cs137.csv'))\n", - "\n", - "Cs137_times = [25.497, 24.604, 24.756, 24.517, 24.732, 24.813, 24.564, 24.710]\n", - "\n", - "single_ch_spectra_cs = [df[ch].tolist() for df, ch in zip(dfs_Cs, ['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16'])]" - ], - "outputs": [], - "execution_count": 42 - }, - { - "cell_type": "code", - "id": "97eb2049-7aab-4804-a96b-555d5d4c84f9", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-13T09:12:36.729790900Z", - "start_time": "2026-02-13T09:12:32.029126600Z" - } - }, - "source": [ - "#Plotting 137Cs spectra\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.2', 'Ch.4', 'Ch.6', 'Ch.8', 'Ch.10', 'Ch.12', 'Ch.14', 'Ch.16']\n", - "for idx,s in enumerate(single_ch_spectra_cs):\n", - " plt.hist(s, N_BINS, range=(0,BITS_12), weights=(1/Cs137_times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Count rate ($s^{-1}$)')\n", - "plt.title('Charge comparator 137Cs spectra, SiPM-Channels 5-8')\n", - "plt.xticks(np.arange(0,4500,500))\n", - "plt.grid()\n", - "#plt.savefig('../figures/QC_SiPMch5-8_8channels_137Cs.png')\n", - "plt.show()" - ], - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 43 - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/CT_Cs137_calib.ipynb b/noteboks/CT_Cs137_calib.ipynb deleted file mode 100644 index d72440e..0000000 --- a/noteboks/CT_Cs137_calib.ipynb +++ /dev/null @@ -1,731 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "1b3d1721-b1d7-4c84-aa16-7d0dd20c2916", - "metadata": {}, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "\n", - "from processing import SiphraAcquisition, fit_peak_expbg\n", - "\n", - "# ROOT.gROOT.SetBatch(False)\n", - "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", - "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", - "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", - "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8d28291e-86ef-46db-903e-fc8a7140859f", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d0ef0cc0-2107-4ad2-ace8-04c4abf3ed95", - "metadata": {}, - "outputs": [], - "source": [ - "BG_PMchs58 = []\n", - "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/1_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=2,\n", - " exposure_sec=119.634,\n", - " sipm_chs=\"5-8\"))\n", - "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=6,\n", - " exposure_sec=161.062,\n", - " sipm_chs=\"5-8\"))\n", - "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=10,\n", - " exposure_sec=148.920,\n", - " sipm_chs=\"5-8\"))\n", - "BG_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=14,\n", - " exposure_sec=164.828,\n", - " sipm_chs=\"5-8\"))\n", - "\n", - "BG_PMchs14 = []\n", - "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv',\n", - " active_chs=2,\n", - " exposure_sec=34.836,\n", - " sipm_chs=\"1-4\"))\n", - "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/6_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=6,\n", - " exposure_sec=80.792,\n", - " sipm_chs=\"1-4\"))\n", - "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/7_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=10,\n", - " exposure_sec=54.386,\n", - " sipm_chs=\"1-4\"))\n", - "BG_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/8_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Background.csv',\n", - " active_chs=14,\n", - " exposure_sec=90.837,\n", - " sipm_chs=\"1-4\"))\n", - "\n", - "Cs137_PMchs14 = []\n", - "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/12_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=2,\n", - " exposure_sec=23.381,\n", - " sipm_chs=\"1-4\"))\n", - "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/11_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=6,\n", - " exposure_sec=24.606,\n", - " sipm_chs=\"1-4\"))\n", - "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/10_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=10,\n", - " exposure_sec=24.144,\n", - " sipm_chs=\"1-4\"))\n", - "Cs137_PMchs14.append(SiphraAcquisition(project_root+'/data/260209/9_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=14,\n", - " exposure_sec=24.645,\n", - " sipm_chs=\"1-4\"))\n", - "\n", - "Cs137_PMchs58 = []\n", - "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/13_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=2,\n", - " exposure_sec=24.720,\n", - " sipm_chs=\"5-8\"))\n", - "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/14_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=6,\n", - " exposure_sec=24.916,\n", - " sipm_chs=\"5-8\"))\n", - "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/15_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=10,\n", - " exposure_sec=24.848,\n", - " sipm_chs=\"5-8\"))\n", - "Cs137_PMchs58.append(SiphraAcquisition(project_root+'/data/260209/16_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv',\n", - " active_chs=14,\n", - " exposure_sec=24.934,\n", - " sipm_chs=\"5-8\"))\n", - "\n", - "datasets_lsts = [BG_PMchs58, Cs137_PMchs58, BG_PMchs14, Cs137_PMchs14]\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kCyan, ROOT.kYellow, ROOT.kSpring, ROOT.kViolet]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fb43e56f-ec35-4690-b0f1-2cef16671a15", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv\n" - ] - }, - { - "data": { - "text/plain": [ - "array([137., 135., 135., ..., 135., 136., 135.], shape=(99976,))" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "current_ds = datasets_lsts[2][0]\n", - "print(current_ds.filepath.name)\n", - "current_ds[16]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "953f73d6-0458-42d5-9cdd-96676945177a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "canvas_raw = []\n", - "cv_names = [\"canvas_raw_\"+str(_) for _ in range(1, 5)]\n", - "for name in cv_names:\n", - " if ROOT.gROOT.FindObject(name):\n", - " ROOT.gROOT.FindObject(name).Close()\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.15)\n", - "\n", - "legends_raw = [ROOT.TLegend(0.65, 0.7, 0.8, 0.88) for _ in range(1,5)]\n", - "Yinit = 0.82 # For stat boxes\n", - "\n", - "hists_raw_dict = {}\n", - "hists_raw_names = [\"hist58Bg\", \"hist58Cs\", \"hist14Bg\", \"hist14Cs\"]\n", - "\n", - "Yinit = 0.82 # For stat boxes\n", - "\n", - "for dataset, name, cv, lg in zip(datasets_lsts, hists_raw_names, cv_names, legends_raw):\n", - " hists_raw_dict[name] = []\n", - " current_hist_list = hists_raw_dict[name]\n", - " canvas_raw.append(ROOT.TCanvas(cv, cv, 800, 600))\n", - " lg.SetHeader('SIPHRA channel')\n", - " # legends_raw.append(ROOT.TLegend(0.65, 0.7, 0.8, 0.88))\n", - " for idx, (acq, color) in enumerate(zip(dataset, colors)):\n", - " # print(f\"Current file: {acq.filepath.name}\")\n", - " ch = acq.active_chs[0]\n", - " hist = ROOT.TH1F(f\"{name}Ch{ch}\", \"\", BITS9, 0, BITS12)\n", - " hist.Fill(acq[ch])\n", - " hist.Scale(1/acq.exposure)\n", - " #Preeting up..\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", - " hist.SetLineColor(color+2)\n", - " hist.SetTitle(name)\n", - " lg.AddEntry(hist, f\"Ch. {ch}\", 'l')\n", - " current_hist_list.append(hist)\n", - " current_hist_list[-1].Draw('sames hist')\n", - " canvas_raw[-1].SetLogy()\n", - " ROOT.gPad.Update()\n", - " for i, sp in enumerate(current_hist_list):\n", - " st = sp.FindObject(\"stats\")\n", - " # print(type(st))\n", - " st.SetY1NDC(Yinit - i*0.08)\n", - " st.SetY2NDC(0.1)\n", - " legends_raw[-1].Draw()\n", - " canvas_raw[-1].Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d66dc8c8-bbac-40db-934c-b55071da1c65", - "metadata": {}, - "outputs": [], - "source": [ - "# if ROOT.gROOT.FindObject(\"canvas\"):\n", - "# ROOT.gROOT.FindObject(\"canvas\").Close()\n", - "# canvas = ROOT.TCanvas(\"canvas\", \"canvas\", 800, 600)\n", - "# ROOT.gStyle.SetOptStat(11)\n", - "# ROOT.gStyle.SetStatFontSize(0.03)\n", - "# ROOT.gStyle.SetStatW(0.15)\n", - "# legend_ary = ROOT.TLegend(0.65, 0.7, 0.8, 0.88)\n", - "\n", - "# Yinit = 0.82 # For stat boxes\n", - "# hists_58BG = []\n", - "# current_dataset = datasets_lsts[2]\n", - "# for acq, color in zip(current_dataset, colors):\n", - "# hist = ROOT.TH1F(f\"h58bgCh{acq.active_chs[0]}\", \"\", BITS9, 0, BITS12)\n", - "# hist.Fill(acq[acq.active_chs[0]])\n", - "# hist.Scale(1/acq.exposure)\n", - "# # Preeting up...\n", - "# hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - "# hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", - "# hist.SetLineColor(color+2)\n", - "# legend_ary.SetHeader('SIPHRA channel')\n", - "# legend_ary.AddEntry(hist, f\"Ch. {acq.active_chs[0]}\", 'l')\n", - "# hists_58BG.append(hist)\n", - "# hists_58BG[-1].Draw(\"sames hist\")\n", - "# canvas.SetLogy()\n", - "\n", - "# ROOT.gPad.Update()\n", - "# Yinit = 0.82\n", - "# for i, sp in enumerate(hists_58BG):\n", - "# st = sp.FindObject(\"stats\")\n", - "# print(type(st))\n", - "# st.SetY1NDC(Yinit - i*0.08)\n", - "# st.SetY2NDC(0.1)\n", - "\n", - "\n", - "# legend_ary.Draw()\n", - "# canvas.Draw()\n", - "# ROOT.gPad.Modified()\n", - "# ROOT.gPad.Update()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7240bc8-54cd-4af5-9187-99770b47829c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ead8479a-3d00-4ccd-980c-dc6c12af58b5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "864.490254507108\n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.454998\n", - "Chi2 = 0.909996\n", - "NDf = 71\n", - "Edm = 4.19258e-06\n", - "NCalls = 254\n", - "Const = 55.1644 +/- 67.0627 \n", - "Denom = 212.972 +/- 54.3249 \n", - "Norm = 0.990514 +/- 0.486902 \n", - "Mean = 863.094 +/- 44.5963 \n", - "Sigma = -68.8679 +/- 41.554 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.361677\n", - "Chi2 = 0.723354\n", - "NDf = 71\n", - "Edm = 9.93854e-07\n", - "NCalls = 237\n", - "Const = 58.8242 +/- 68.9613 \n", - "Denom = 214.571 +/- 52.9045 \n", - "Norm = 0.986847 +/- 0.505386 \n", - "Mean = 861.59 +/- 46.1117 \n", - "Sigma = -65.6188 +/- 43.9628 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.363845\n", - "Chi2 = 0.72769\n", - "NDf = 71\n", - "Edm = 1.69107e-07\n", - "NCalls = 242\n", - "Const = 66.9958 +/- 59.986 \n", - "Denom = 212.005 +/- 43.9622 \n", - "Norm = 0.905532 +/- 0.437406 \n", - "Mean = 900.19 +/- 43.1657 \n", - "Sigma = 68.4399 +/- 39.4644 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.321441\n", - "Chi2 = 0.642883\n", - "NDf = 71\n", - "Edm = 2.84474e-06\n", - "NCalls = 284\n", - "Const = 53.0888 +/- 62.0095 \n", - "Denom = 219.835 +/- 55.2175 \n", - "Norm = 1.02893 +/- 0.500587 \n", - "Mean = 864.49 +/- 44.1728 \n", - "Sigma = 67.5654 +/- 42.4445 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Deleting canvas with same name: canvas\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "canvas = ROOT.TCanvas(\"canvas\", \"canvas\", 800, 600)\n", - "for hist in hists_58BG:\n", - " fit_fn, fit_result = fit_peak_expbg(hist=hist, name='k40peak', xl=700, xr=1300, norm=1, mean=870, showFit=True, keep_prev_fncs=False)\n", - " # hist.Draw('sames hist')\n", - "for hist in hists_58BG: # If this is inside the first for loop, nothing is plotted\n", - " hist.Draw('sames hist')\n", - "canvas.SetLogy()\n", - "canvas.Draw()\n", - "print(fit_fn.GetParameter('Mean'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "836c07b7-c48f-4138-98e9-014138705c4a", - "metadata": {}, - "outputs": [], - "source": [ - "for element in hists_raw_dict.values():\n", - " print(element)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ca2353c-1411-45fb-b29a-5c8057105833", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import namedtuple\n", - "\n", - "BGPeaks = namedtuple('BGPeaks', ('k40', 'cs137'))\n", - "\n", - "for hist_list in [hists_raw_dict[hists_58BG], hists_raw_dict[hists_]:\n", - " for hist in hist_list:\n", - " fit_peak_expbg(hist=hist, name='k40peak', xl=700, xr=1300, norm=1, mean=870, showFit=False, keep_prev_fncs=True)\n", - " fit_peak_expbg(hist=hist, name='cs137peak', xl=1200, xr=1800, norm=0.3, mean=1400, showFit=False, keep_prev_fncs=True)\n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (ROOT in Conda)", - "language": "python", - "name": "comcube" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/QT_compare_channels.ipynb b/noteboks/QT_compare_channels.ipynb deleted file mode 100644 index c7e14aa..0000000 --- a/noteboks/QT_compare_channels.ipynb +++ /dev/null @@ -1,532 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "39965dbb-a83f-4f1b-b012-9ea2c0e0c583", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "08bef2cf-8af2-4047-8fe7-9a7864fa77d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
0550553920315496032233342069149150189106...13114012311812012010813622056
1550554021315812152233342069149149220104...12714012211612011910813622071
25505541203158121522333420691491511413106...13114312511812212010813723292
3550554221316243792233342069149149895105...12913912411912012010813622760
4550554321316534072233342069149150268104...12914212211812012010813522128
..................................................................
9989756055332154601362272942069149150143104...12913912311711911810813562047
9989856055342154705942272942069149149142104...13114112411812011910813562120
9989956055352054705942272942069149150143105...13114112411812012010913662164
9990056055362155812442272942069149150143105...13013912311812012010813542030
9990156055372156869632272942069149150143104...13014112311712112010813762342
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99902 rows × 25 columns

\n", - "
" - ], - "text/plain": [ - " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 \\\n", - "0 5 505539 20 31549603 22333 42069 149 150 \n", - "1 5 505540 21 31581215 22333 42069 149 149 \n", - "2 5 505541 20 31581215 22333 42069 149 151 \n", - "3 5 505542 21 31624379 22333 42069 149 149 \n", - "4 5 505543 21 31653407 22333 42069 149 150 \n", - "... ... ... ... ... ... ... ... ... \n", - "99897 5 605533 21 5460136 22729 42069 149 150 \n", - "99898 5 605534 21 5470594 22729 42069 149 149 \n", - "99899 5 605535 20 5470594 22729 42069 149 150 \n", - "99900 5 605536 21 5581244 22729 42069 149 150 \n", - "99901 5 605537 21 5686963 22729 42069 149 150 \n", - "\n", - " Ch2 Ch3 ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax \\\n", - "0 189 106 ... 131 140 123 118 120 120 108 136 2 \n", - "1 220 104 ... 127 140 122 116 120 119 108 136 2 \n", - "2 1413 106 ... 131 143 125 118 122 120 108 137 2 \n", - "3 895 105 ... 129 139 124 119 120 120 108 136 2 \n", - "4 268 104 ... 129 142 122 118 120 120 108 135 2 \n", - "... ... ... ... ... ... ... ... ... ... ... ... ... \n", - "99897 143 104 ... 129 139 123 117 119 118 108 135 6 \n", - "99898 142 104 ... 131 141 124 118 120 119 108 135 6 \n", - "99899 143 105 ... 131 141 124 118 120 120 109 136 6 \n", - "99900 143 105 ... 130 139 123 118 120 120 108 135 4 \n", - "99901 143 104 ... 130 141 123 117 121 120 108 137 6 \n", - "\n", - " Summed \n", - "0 2056 \n", - "1 2071 \n", - "2 3292 \n", - "3 2760 \n", - "4 2128 \n", - "... ... \n", - "99897 2047 \n", - "99898 2120 \n", - "99899 2164 \n", - "99900 2030 \n", - "99901 2342 \n", - "\n", - "[99902 rows x 25 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "dfs = []\n", - "dfs.append(pd.read_csv('../data/260203/5_SiPM_ChannelsTest_Ch1-4_Ch2_QT_Thr20_Hys0_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260203/6_SiPM_ChannelsTest_Ch1-4_Ch6_QT_Thr20_Hys0_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260203/7_SiPM_ChannelsTest_Ch1-4_Ch10_QT_Thr20_Hys0_Background.csv'))\n", - "dfs.append(pd.read_csv('../data/260203/8_SiPM_ChannelsTest_Ch1-4_Ch14_QT_Thr20_Hys0_Background.csv'))\n", - "\n", - "dfs[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1f49e311-fe5b-4144-b076-94ef58c00cfd", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "N_BINS = 512\n", - "BITS_12 = 2**12\n", - "\n", - "summed_spectra = [df['Summed'].tolist() for df in dfs]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "3ed406a4-063e-4356-96b3-c1618bc5acfc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [64.392, 113.229, 82.507, 45.958]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(1800, 4000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Events rate ($s^{-1}$)')\n", - "# plt.xticks(np.arange(1800,4000,100))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "1d5d4034-c260-4380-9799-069c3dd46ccc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dfs.append(pd.read_csv('../data/260203/9_SiPM_ChannelsTest_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv'))\n", - "summed_spectra.append(dfs[4]['Summed'])\n", - "times_ch14 = [times[3], 137.539]\n", - "legend = ['SiPM Ch.1-4', 'SiPM Ch.5-8']\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "for idx,s in enumerate([summed_spectra[3],summed_spectra[4]]):\n", - " plt.hist(s, N_BINS, range=(1800, 8000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.title('SIPHRA Ch14 active summed spectra, QT')\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Events rate ($s^{-1}$)')\n", - "# plt.xticks(np.arange(1800,4000,100))\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "4c130d5f-d182-4d99-b629-a3e03dfb1e66", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ch14_spectra = [dfs[3]['Ch14'], dfs[4]['Ch14']]\n", - "times_ch14 = [times[3], 137.539]\n", - "legend = ['SiPM Ch.1-4', 'SiPM Ch.5-8']\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "for idx,s in enumerate([ch14_spectra[0],ch14_spectra[1]]):\n", - " plt.hist(s, N_BINS, range=(0, 4500), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.title('SIPHRA Ch.14 active single-channel spectra, QT')\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Events rate ($s^{-1}$)')\n", - "# plt.xticks(np.arange(1800,4000,100))\n", - "plt.grid()\n", - "plt.show()" - ] - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/Untitled.ipynb b/noteboks/Untitled.ipynb deleted file mode 100644 index dbabf7d..0000000 --- a/noteboks/Untitled.ipynb +++ /dev/null @@ -1,229 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b2e685cc-738a-4b98-9983-3fb32e245a15", - "metadata": {}, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "\n", - "from processing import SiphraAcquisition\n", - "from analysis import fit_peak_expbg" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a5e76308-c39b-410f-934c-8b40b81c8630", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "CS137_MEV = 0.661\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9196704d-b0fc-4fc8-a350-9ae2cba0d208", - "metadata": {}, - "outputs": [], - "source": [ - "channels = [f'Ch{n}' for n in range(2,17,2)]\n", - "files_lsts = {}\n", - "files_lsts[channels[0]] = sorted(Path(project_root+'/data/260311').glob('SUBTRACTED*Ch2*.csv'))\n", - "files_lsts[channels[0]].pop(3)\n", - "files_lsts[channels[0]].pop(3)\n", - "files_lsts[channels[0]].pop(3)\n", - "for ch in channels[1:]:\n", - " files_lsts[ch] = sorted(Path(project_root+'/data/260311').glob(f'SUBTRACTED*{ch}*.csv'))\n", - "\n", - "vbiases = [28.0, 28.5, 29.0]\n", - "# Datasets\n", - "datasets = {}\n", - "for ch, lst in files_lsts.items():\n", - " datasets[ch] = []\n", - " datasets[ch].append(SiphraAcquisition(lst[2]))\n", - " datasets[ch].append(SiphraAcquisition(lst[0]))\n", - " datasets[ch].append(SiphraAcquisition(lst[1]))\n", - "del(files_lsts)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "540e967f-8cf8-4abb-9884-1c13966b45f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Ch2': [SIPHRAAcquisition(File: 'SUBTRACTED_17_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch2_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_01_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch2_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_09_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch2_Cs137')],\n", - " 'Ch4': [SIPHRAAcquisition(File: 'SUBTRACTED_18_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch4_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_02_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch4_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_10_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch4_Cs137')],\n", - " 'Ch6': [SIPHRAAcquisition(File: 'SUBTRACTED_19_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch6_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_03_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch6_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_11_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch6_Cs137')],\n", - " 'Ch8': [SIPHRAAcquisition(File: 'SUBTRACTED_20_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch8_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_04_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch8_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_12_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch8_Cs137')],\n", - " 'Ch10': [SIPHRAAcquisition(File: 'SUBTRACTED_21_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch10_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_05_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch10_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_13_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch10_Cs137')],\n", - " 'Ch12': [SIPHRAAcquisition(File: 'SUBTRACTED_22_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch12_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_06_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch12_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_14_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch12_Cs137')],\n", - " 'Ch14': [SIPHRAAcquisition(File: 'SUBTRACTED_23_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch14_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_07_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch14_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_15_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch14_Cs137')],\n", - " 'Ch16': [SIPHRAAcquisition(File: 'SUBTRACTED_24_SiPM_quadrant4_Vbias28,0_SummingBoardPos1_Ch16_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_08_SiPM_quadrant4_Vbias28,5_SummingBoardPos1_Ch16_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBTRACTED_16_SiPM_quadrant4_Vbias29,0_SummingBoardPos1_Ch16_Cs137')]}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "0cebeb17-52de-4f4c-981c-5409cdc95ca8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "31.89442729949951" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datasets['Ch2'][0].exposure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e038aa17-88a8-4102-a198-7cd1e4412e96", - "metadata": {}, - "outputs": [], - "source": [ - "# Histograms\n", - "\n", - "for ch, dataset in datasets.items():\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9ceb181-d450-4ace-b064-996b01af676c", - "metadata": {}, - "outputs": [], - "source": [ - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "cv = ROOT.TCanvas('cv', 'cv', 2000, 1200)\n", - "cv.divide(4,2)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "hists = {}\n", - "legends = {}\n", - "\n", - "for idx, (ch, dataset) in enumerate(datasets.items()):\n", - " cv.cd(idx)\n", - " hists[ch] = []\n", - " lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", - " for acq, vbias in zip(dataset, vbiases):\n", - " hist = ROOT.TH1F(f\"{ch} Vbias={vbias}\", \"\", BITS12, 0, BITS12)\n", - " hist.Fill(acq[ch])\n", - " hist.Scale(1/acq.exposure)\n", - " hists[ch].append(hist)\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Counts\")\n", - " hist.SetLineColor(colors[idx])\n", - " del(hist)\n", - " \n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "3ef60607-4f0e-4336-83d6-ab91f65b2189", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "cannot use 'list' as a set element (unhashable type: 'list')", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m ll = [(\u001b[33m'\u001b[39m\u001b[33ma\u001b[39m\u001b[33m'\u001b[39m,\u001b[32m1\u001b[39m), (\u001b[33m'\u001b[39m\u001b[33mb\u001b[39m\u001b[33m'\u001b[39m,\u001b[32m2\u001b[39m)]\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m ll_dict = {ll}\n", - "\u001b[31mTypeError\u001b[39m: cannot use 'list' as a set element (unhashable type: 'list')" - ] - } - ], - "source": [ - "ll = [('a',1), ('b',2)]\n", - "ll_dict = {ll}" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (ROOT in Conda)", - "language": "python", - "name": "comcube" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/Voltage_dependency.ipynb b/noteboks/Voltage_dependency.ipynb deleted file mode 100644 index a5290db..0000000 --- a/noteboks/Voltage_dependency.ipynb +++ /dev/null @@ -1,695 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248d5111-9526-4226-b6ef-a67dc0c71445", - "metadata": {}, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "\n", - "from processing import SiphraAcquisition\n", - "from analysis import fit_peak_expbg\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c9421d1e-23fe-4585-905c-2f56603eafca", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "Cs237_MEV = 0.662" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a1ebd8ed-1b60-42dd-b3d9-2372f7596aad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Ch2', 'Ch4', 'Ch6', 'Ch8', 'Ch10', 'Ch12', 'Ch14', 'Ch16']\n" - ] - } - ], - "source": [ - "#Make sure json files have matching names to the dat files. \n", - "\n", - "channels = [f'Ch{n}' for n in range(2,17,2)]\n", - "print(channels)\n", - "files_lsts = {}\n", - "files_lsts[channels[0]] = sorted(Path(project_root+'/data/260311').glob('SUBTRACTED*Ch2*.csv'))\n", - "files_lsts[channels[0]].pop(3)\n", - "files_lsts[channels[0]].pop(3)\n", - "files_lsts[channels[0]].pop(3)\n", - "for ch in channels[1:]:\n", - " files_lsts[ch] = sorted(Path(project_root+'/data/260311').glob(f'SUBTRACTED*{ch}*.csv'))\n", - "\n", - "vbiases = [28.0, 28.5, 29.0]\n", - "# Datasets\n", - "datasets = {}\n", - "for ch, lst in files_lsts.items():\n", - " datasets[ch] = []\n", - " datasets[ch].append(SiphraAcquisition(lst[2]))\n", - " datasets[ch].append(SiphraAcquisition(lst[0]))\n", - " datasets[ch].append(SiphraAcquisition(lst[1]))\n", - "\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kCyan, ROOT.kYellow, ROOT.kSpring, ROOT.kViolet]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4f82a8be-e799-4405-9464-bb085d20cdf3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
0520553921239503771882042069149150176104...13014212311712011310713522032
1520554021239589821882042069149150175103...1291411221181209710713522010
2520554120239589821882042069149150181103...12914012211712013310713622047
3520554221239778241882042069149149246103...12914012311712012910713622109
4520554320239778241882042069149149157103...129141122117121172107135142066
..................................................................
99959530553320352773941902242069149151236103...12714012311712011510713522086
99960530553420352773941902242069149150215104...13113912311812115010813622107
99961530553520352773941902242069149149266102...13014012311812114010713522141
99962530553620352773941902242069149151213104...13014012311811910410813722062
99963530553721353542541902242069149150203103...13014012411612013510813622073
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99964 rows × 25 columns

\n", - "
" - ], - "text/plain": [ - " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 \\\n", - "0 5 205539 21 23950377 18820 42069 149 150 \n", - "1 5 205540 21 23958982 18820 42069 149 150 \n", - "2 5 205541 20 23958982 18820 42069 149 150 \n", - "3 5 205542 21 23977824 18820 42069 149 149 \n", - "4 5 205543 20 23977824 18820 42069 149 149 \n", - "... ... ... ... ... ... ... ... ... \n", - "99959 5 305533 20 35277394 19022 42069 149 151 \n", - "99960 5 305534 20 35277394 19022 42069 149 150 \n", - "99961 5 305535 20 35277394 19022 42069 149 149 \n", - "99962 5 305536 20 35277394 19022 42069 149 151 \n", - "99963 5 305537 21 35354254 19022 42069 149 150 \n", - "\n", - " Ch2 Ch3 ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax \\\n", - "0 176 104 ... 130 142 123 117 120 113 107 135 2 \n", - "1 175 103 ... 129 141 122 118 120 97 107 135 2 \n", - "2 181 103 ... 129 140 122 117 120 133 107 136 2 \n", - "3 246 103 ... 129 140 123 117 120 129 107 136 2 \n", - "4 157 103 ... 129 141 122 117 121 172 107 135 14 \n", - "... ... ... ... ... ... ... ... ... ... ... ... ... \n", - "99959 236 103 ... 127 140 123 117 120 115 107 135 2 \n", - "99960 215 104 ... 131 139 123 118 121 150 108 136 2 \n", - "99961 266 102 ... 130 140 123 118 121 140 107 135 2 \n", - "99962 213 104 ... 130 140 123 118 119 104 108 137 2 \n", - "99963 203 103 ... 130 140 124 116 120 135 108 136 2 \n", - "\n", - " Summed \n", - "0 2032 \n", - "1 2010 \n", - "2 2047 \n", - "3 2109 \n", - "4 2066 \n", - "... ... \n", - "99959 2086 \n", - "99960 2107 \n", - "99961 2141 \n", - "99962 2062 \n", - "99963 2073 \n", - "\n", - "[99964 rows x 25 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "dfs = []\n", - "dfs.append(pd.read_csv('../data/260203/4_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Banana.csv'))\n", - "dfs.append(pd.read_csv('../data/260203/3_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv'))\n", - "\n", - "dfs[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b86f145e-0b76-4aaa-8efb-2d2809a4a622", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "N_BINS = 512\n", - "BITS_12 = 2**12\n", - "\n", - "summed_spectra = [df['Summed'].tolist() for df in dfs]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7b1167b3-85d2-4726-945f-29bc63241135", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [52.526, 50.871]\n", - "plt.figure(figsize=(10,10), dpi=80)\n", - "legend = ['Background', 'Banana']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(0, 10000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel(r'Energy channel')\n", - "plt.ylabel(r'Events rate ($s^{-1}$)')\n", - "plt.title('Ch.2 + Ch.14 Background and bBnana spectra')\n", - "# plt.xticks(np.arange(1800,4000,100))\n", - "plt.grid()\n", - "plt.show()" - ] - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/baseline_subtraction.ipynb b/noteboks/baseline_subtraction.ipynb deleted file mode 100644 index ced9272..0000000 --- a/noteboks/baseline_subtraction.ipynb +++ /dev/null @@ -1,1143 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cda6a9c1-c4c0-4e69-b50d-7dcb1b8434f2", - "metadata": {}, - "source": [ - "# Performing baselie subtraction\n", - "\n", - "The baseline-subtracted histograms of an acquisition with 2 active channels were obtained. The baseline value for each channel is the mean of the readings from all the events triggered ONLY by external HOLD assertion. This value is subtracted from the values of each \"real\" event (triggered from internal HOLD).\n", - "\n", - "The dataset for this analysis is a background acquisition of 10 000 events. SIPHRA channels 2 and 14 were connected to SiPM channels 1-4 and 5-8, respectively, using charge comparator, with a threshold value of 20." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:37:32.276396Z", - "start_time": "2026-02-10T09:37:32.169808Z" - } - }, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "\n", - "from processing import SiphraAcquisition\n", - "from analysis import fit_peak_expbg\n", - "\n", - "# ROOT.gROOT.SetBatch(False)\n", - "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", - "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", - "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", - "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7aa83758-b8f5-4a20-96c3-c8381ea71536", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "CS137_MEV = 0.661\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "68a9cd08dc972f65", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:37:35.457562Z", - "start_time": "2026-02-10T09:37:33.669215Z" - } - }, - "outputs": [], - "source": [ - "# Datasets\n", - "acq_notSubtracted = SiphraAcquisition(project_root+'/data/260203/1_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv',\n", - " active_chs=[2,14],\n", - " exposure_sec=1)\n", - "acq_subtracted = SiphraAcquisition(project_root+'/data/260203/1_datdecodertest_subtractBaseline.csv',\n", - " active_chs=[2,14],\n", - " exposure_sec=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cae21a3ee684d69f", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:43:25.401070Z", - "start_time": "2026-02-10T09:43:24.995723Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "SUMMED_XR=5000 # Right limit of the summed channel's x-axis\n", - "\n", - "c = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "names=['BL subt.', 'No correction']\n", - "hists = []\n", - "\n", - "lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", - "\n", - "Yinit = 0.82 # For stat boxes\n", - "\n", - "for idx, (acq, name) in enumerate(zip([acq_subtracted, acq_notSubtracted], names)):\n", - " # print(f\"Current file: {acq.filepath.name}\")\n", - " ch = 'Summed' #acq.active_chs[0]\n", - " hist = ROOT.TH1F(f\"{ch} {name}\", \"\", int(BITS9*SUMMED_XR/BITS12), 0, SUMMED_XR)\n", - " hist_singlech = ROOT.TH1F(f\"Ch. {acq.active_chs[0]} {name}\", \"\", BITS9, 0, BITS12)\n", - " hist.Fill(acq[ch]/len(acq.active_chs))\n", - " hist_singlech.Fill(acq[acq.active_chs[0]])\n", - " hist.Scale(1/acq.exposure)\n", - " hist_singlech.Scale(1/acq.exposure)\n", - "\n", - " hist_singlech2 = ROOT.TH1F(f\"Ch. {acq.active_chs[1]} {name}\", \"\", BITS9, 0, BITS12)\n", - " hist_singlech2.Fill(acq[acq.active_chs[1]])\n", - " hist_singlech2.Scale(1/acq.exposure)\n", - " #Preeting up..\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Counts\")\n", - " hist.SetLineColor(colors[idx])\n", - " hist.SetTitle(\"Baseline subtraction comparison\")\n", - " # hist.GetYaxis().SetRangeUser(0, 1e4)\n", - " lg.SetHeader(\"SIPHRA Channel\")\n", - " lg.AddEntry(hist, f\"{ch} {name}\", 'l')\n", - " hists.append(hist)\n", - " hists[-1].Draw('sames hist')\n", - " # hist_singlech.GetXaxis().SetTitle(\"ADC channel number\")\n", - " # hist_singlech.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", - " hist_singlech.SetLineColor(colors[idx + 2]+2)\n", - " hist_singlech2.SetLineColor(colors[idx + 4]+1)\n", - " # hist_singlech.SetTitle(\"Baseline subtraction comparison\")\n", - " lg.AddEntry(hist_singlech, f\"Ch. {acq.active_chs[0]} {name}\", 'l')\n", - " lg.AddEntry(hist_singlech2, f\"Ch. {acq.active_chs[1]} {name}\", 'l')\n", - " hists.append(hist_singlech)\n", - " hists[-1].Draw('sames hist')\n", - " hists.append(hist_singlech2)\n", - " hists[-1].Draw('sames hist')\n", - "c.SetLogy()\n", - "ROOT.gPad.Update()\n", - "for i, sp in enumerate(hists):\n", - " st = sp.FindObject(\"stats\")\n", - " # print(type(st))\n", - " st.SetY1NDC(Yinit - i*0.08)\n", - " st.SetY2NDC(0.1)\n", - "lg.Draw()\n", - "c.Draw()\n", - "ROOT.gPad.Modified()\n", - "ROOT.gPad.Update()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4fe40546-908d-46cd-868c-f4c3e9bd0902", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject('cv_4plots'):\n", - " ROOT.gROOT.FindObject('cv_4plots').Close()\n", - "\n", - "canvas4 = ROOT.TCanvas('cv_4plots', 'cv_4plots', 1600, 1200)\n", - "canvas4.Divide(2,2)\n", - "\n", - "legends = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", - "\n", - "canvas4.cd(1)\n", - "hists[0].Draw('hist')\n", - "hists[3].Draw('sames hist')\n", - "legends[0].AddEntry(hists[0], \"Baseline subtracted\", 'l')\n", - "legends[0].AddEntry(hists[3], \"Not corrected\", 'l')\n", - "legends[0].SetHeader(\"\\'Summed\\' channel spectra\")\n", - "legends[0].Draw()\n", - "canvas4.cd(1).SetLogy()\n", - "\n", - "canvas4.cd(2)\n", - "hists[1].SetXTitle(\"ADC channel number\")\n", - "hists[1].SetYTitle(\"Counts\")\n", - "hists[1].Draw('hist')\n", - "hists[4].Draw('sames hist')\n", - "legends[1].AddEntry(hists[1], \"Baseline subtracted\", 'l')\n", - "legends[1].AddEntry(hists[4], \"Not corrected\", 'l')\n", - "legends[1].SetHeader(\"Ch. 2 spectra\")\n", - "legends[1].Draw()\n", - "canvas4.cd(2).SetLogy()\n", - "\n", - "canvas4.cd(3)\n", - "hists[2].SetXTitle(\"ADC channel number\")\n", - "hists[2].SetYTitle(\"Counts\")\n", - "hists[2].Draw('hist')\n", - "hists[5].Draw('sames hist')\n", - "legends[2].AddEntry(hists[2], \"Baseline subtracted\", 'l')\n", - "legends[2].AddEntry(hists[5], \"Not corrected\", 'l')\n", - "legends[2].SetHeader(\"Ch. 14 spectra\")\n", - "legends[2].Draw()\n", - "canvas4.cd(3).SetLogy()\n", - "\n", - "canvas4.cd(4)\n", - "h0_zoom = hists[0].Clone(\"hists_0_zoomed\")\n", - "h0_zoom.GetXaxis().SetRangeUser(0,BITS12)\n", - "h0_zoom.Draw('hist')\n", - "hists[1].Draw('sames hist')\n", - "hists[2].Draw('sames hist')\n", - "legends[3].AddEntry(h0_zoom, \"\\'Summed\\'\", 'l')\n", - "legends[3].AddEntry(hists[1], \"Ch. 2\", 'l')\n", - "legends[3].AddEntry(hists[2], \"Ch. 14\", 'l')\n", - "legends[3].SetHeader(\"Corrected channels\")\n", - "legends[3].Draw()\n", - "canvas4.cd(4).SetLogy()\n", - "\n", - "canvas4.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "46bd074b-d5aa-48a4-b027-f966314995cd", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ADC channels range:\n", - "--------------------------------------------------\n", - "Channel\t\tBL-subtracted\tNo correction\n", - "--------------------------------------------------\n", - "Summed\t\t0.0 - 3965.0\t935.5 - 4880.5\n", - "Ch. 2\t\t0.0 - 3951.0\t51.0 - 4095.0\n", - "Ch. 14\t\t0.0 - 3979.0\t0.0 - 4095.0\n", - "\n", - "\n", - "\n", - "Number of values above single channel range in 'Summed' channel:\n", - "------------------------------\n", - "BL-subtracted\tNo correction\n", - "------------------------------\n", - " 0 \t 22 \n" - ] - } - ], - "source": [ - "print(\"ADC channels range:\\n\"\n", - " f\"{\"\":->50}\\n\"\n", - " \"Channel\\t\\tBL-subtracted\\tNo correction\\n\"\n", - " f\"{\"\":->50}\\n\"\n", - " f\"Summed\\t\\t{acq_subtracted['s'].min()/2} - {acq_subtracted['s'].max()/2}\\t{acq_notSubtracted['s'].min()/2} - {acq_notSubtracted['s'].max()/2}\\n\"\n", - " f\"Ch. 2\\t\\t{acq_subtracted[2].min()} - {acq_subtracted[2].max()}\\t{acq_notSubtracted[2].min()} - {acq_notSubtracted[2].max()}\\n\"\n", - " f\"Ch. 14\\t\\t{acq_subtracted[14].min()} - {acq_subtracted[14].max()}\\t{acq_notSubtracted[14].min()} - {acq_notSubtracted[14].max()}\\n\")\n", - "\n", - "print(\"\\n\\n\"\n", - " \"Number of values above single channel range in \\'Summed\\' channel:\\n\"\n", - " f\"{\"\":->30}\\n\"\n", - " \"BL-subtracted\\tNo correction\\n\"\n", - " f\"{\"\":->30}\\n\"\n", - " f\"{len(acq_subtracted['s'][acq_subtracted['s'][:]/2>BITS12]):^13}\\t{len(acq_notSubtracted['s'][acq_notSubtracted['s'][:]/2>BITS12]):^13}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ae86c5f0-762f-4cca-a8bb-b0c00694959e", - "metadata": {}, - "source": [ - "The baseline subtraction is performed directly at the conversion script. No averaging over the number of channels is carried out by the conversion script, but the above spectra and data correspond to values averaged over the number of active channels (2).\n", - "\n", - "The values of the baselines of all the channels are between 98 and 150.\n", - "\n", - "The range of the summed channel does not exceed the maximum single-channel value (4096) after baseline correction.\n", - "\n", - "**Questions:**\n", - "- Is this baseline correction enough for pedestal subtraction?\n", - "- What about the large peak at the lower range of the spectrum? Is it still originating from noise at this stage?" - ] - }, - { - "cell_type": "markdown", - "id": "19798694-0ef8-43fb-a09c-6b87cc8b03f5", - "metadata": {}, - "source": [ - "# Calibration of corrected spectrum with the backgrond $^{40}$K and $^{208}$Tl peaks\n", - "\n", - "The current process of calibration to actual energies is shown here. The two peaks of $^{40}$K and $^{208}$Tl present in the background are used for this purpose. The emission lines of these nuclides are located at 1.460 MeV and 2.614 MeV, respectively. First, both peaks are fitted to a Gaussian with exponentially-decaying background in the baseline-subtracted background histogram. The Gaussian means are taken as the actual locations of the emission lines. The raw data is linearly transformed to aligh with these two points.\n", - "\n", - "To verify the calibration, we use the same calibration parameters to fit a spectrum taken in the pressence of a sample of $^{137}$Cs. After background subtraction, it is expected that the mean of the Gaussian fit of the resulting peak, lands at 0.661MeV." - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "a4f39db9-ad0d-4f5b-b3b5-ea67040593a6", - "metadata": {}, - "outputs": [], - "source": [ - "# Datasets\n", - "folder = Path(project_root)/'data/260209'\n", - "times_lst = [0, 119.634, 161.062, 148.920, 164.828, 34.836, 80.792, 54.386, 90.837, 24.645, 24.144, 24.606, 23.381, 24.720, 24.916, 24.848, 24.934] # Index coincide with starting number of file name\n", - "\n", - "# Select SiPM channels 5-8, SIPHRA channel 14 -> gives cleaner signal\n", - "# Background is file with #4, source is file with #16, with preffix 'BLsubtr_'\n", - "BGIDX = 4; SRCIDX=16; ACTIVECH=14\n", - "bgfile = sorted(folder.glob(f'BLsubtr_{BGIDX}*.csv'))[0]\n", - "srcfile = sorted(folder.glob(f'BLsubtr_{SRCIDX}*.csv'))[0]\n", - "\n", - "acq_bg = SiphraAcquisition(bgfile, active_chs=ACTIVECH, exposure_sec=times_lst[BGIDX])\n", - "acq_src = SiphraAcquisition(srcfile, active_chs=ACTIVECH, exposure_sec=times_lst[SRCIDX])\n", - "colors_clb = [colors[1]-1, colors[3]-1, colors[0]-2] # colors for BG, signal and clean, respectively\n", - "NORMFACTOR = acq_src.exposure/(acq_bg.exposure)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0b44ed9c-6f45-4174-8f81-1407bcdacc05", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ADC channels range after baseline subtraction:\n", - "CHANNEL 14\n", - "------------------------------\n", - "Data set\tRange\n", - "------------------------------\n", - "Background\t0.0 - 3924.0\n", - "Source\t\t0.0 - 3797.0\n", - "\n", - "SUMMED CHANNEL\n", - "------------------------------\n", - "Data set\tRange\n", - "------------------------------\n", - "Background\t0.0 - 3924.0\n", - "Source\t\t0.0 - 3797.0\n", - "\n" - ] - } - ], - "source": [ - "print(\"ADC channels range after baseline subtraction:\\n\"\n", - " \"CHANNEL 14\\n\"\n", - " f\"{\"\":->30}\\n\"\n", - " \"Data set\\tRange\\n\"\n", - " f\"{\"\":->30}\\n\"\n", - " f\"Background\\t{acq_bg[ACTIVECH].min()} - {acq_bg[ACTIVECH].max()}\\n\"\n", - " f\"Source\\t\\t{acq_src[ACTIVECH].min()} - {acq_src[ACTIVECH].max()}\\n\\n\"\n", - " \"SUMMED CHANNEL\\n\"\n", - " f\"{\"\":->30}\\n\"\n", - " \"Data set\\tRange\\n\"\n", - " f\"{\"\":->30}\\n\"\n", - " f\"Background\\t{acq_bg['s'].min()} - {acq_bg['s'].max()}\\n\"\n", - " f\"Source\\t\\t{acq_src['s'].min()} - {acq_src['s'].max()}\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "cddfadfd-d1a7-4257-9712-15c6daf6d565", - "metadata": {}, - "source": [ - "Baseline subtraction is effectively filtering out noise from inactive channels. It is noteworthy that, when subtracting single channels, the upper ADC channels will be empty, hence there is a loss of dynamic range due to baseline subtraction." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "2093b302-1f12-4e19-86f6-74506d4e5ff6", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Replacing existing TH1: Background (Potential memory leak).\n", - "Warning in : Replacing existing TH1: Signal 137Cs (Potential memory leak).\n" - ] - } - ], - "source": [ - "# Raw histograms\n", - "hist_bg = ROOT.TH1F(\"Background\", \"\", BITS9, 0, BITS12)\n", - "hist_bg.Fill(acq_bg[ACTIVECH])\n", - "hist_bg.Scale(NORMFACTOR) # Normalize counts to the source exposure time\n", - "\n", - "hist_src = ROOT.TH1F(\"Signal 137Cs\", \"\", BITS9, 0, BITS12)\n", - "hist_src.Fill(acq_src[ACTIVECH])\n", - "\n", - "# Bacground subtraction\n", - "hist_clean = hist_src.Clone(\"137Cs no BG\")\n", - "hist_clean.Add(hist_bg, -1)\n", - "# Removing negative bins\n", - "for i in range(1, hist_clean.GetNbinsX() + 1): # bin 0 is the underflow\n", - " if hist_clean.GetBinContent(i) < 0: hist_clean.SetBinContent(i, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "eacf6495-684b-42fa-9eab-8ca73fb2afef", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Error in : Cannot set Y axis to log scale\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot all the histograms\n", - "if ROOT.gROOT.FindObject('cv1'):\n", - " ROOT.gROOT.FindObject('cv1').Close()\n", - "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", - "cv1.Divide(2,1)\n", - "\n", - "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", - "cv1.cd(1)\n", - "hist_bg.GetXaxis().SetTitle(\"ADC Channel\")\n", - "hist_bg.GetYaxis().SetTitle(\"Normalized counts\")\n", - "hist_bg.SetLineColor(colors_clb[0])\n", - "hist_bg.SetFillColor(colors_clb[0])\n", - "hist_src.SetLineColor(colors_clb[1])\n", - "hist_src.SetFillColorAlpha(colors_clb[1], 0.8)\n", - "lg1.AddEntry(hist_bg, \"Background\", \"l\")\n", - "lg1.AddEntry(hist_src, r\"Signal ^{137}Cs\", \"l\")\n", - "hist_bg.Draw(\"hist\")\n", - "hist_src.Draw(\"sames hist\")\n", - "lg1.Draw()\n", - "cv1.cd(1).SetLogy()\n", - "hist_bg.GetYaxis().SetRangeUser(0,1e4)\n", - "\n", - "\n", - "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", - "cv1.cd(2)\n", - "hist_clean.GetXaxis().SetTitle(\"ADC Channel\")\n", - "hist_clean.GetYaxis().SetTitle(\"Normalized counts\")\n", - "hist_clean.SetLineColor(colors_clb[2])\n", - "hist_clean.SetFillColor(colors_clb[2])\n", - "hist_clean.Draw(\"hist\")\n", - "cv1.cd(2).SetLogy()\n", - "\n", - "cv1.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "1b94d6b2-ea7d-41ea-acb2-a7e820e9dd34", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 3.07992\n", - "Chi2 = 6.15985\n", - "NDf = 45\n", - "Edm = 4.33544e-06\n", - "NCalls = 378\n", - "Const = 967.676 +/- 315.198 \n", - "Denom = 204.714 +/- 16.8531 \n", - "Norm = 26.0236 +/- 2.92104 \n", - "Mean = 748.998 +/- 10.0065 \n", - "Sigma = 69.1169 +/- 10.4622 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 4.22284\n", - "Chi2 = 8.44567\n", - "NDf = 45\n", - "Edm = 1.92878e-06\n", - "NCalls = 607\n", - "Const = 65.7641 +/- 57.5374 \n", - "Denom = 441.486 +/- 153.51 \n", - "Norm = 1.79752 +/- 0.994549 \n", - "Mean = 1240.31 +/- 25.9607 \n", - "Sigma = 44.4995 +/- 28.325 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Deleting canvas with same name: cv_fit\n", - "Error in : Cannot set Y axis to log scale\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Fit background peaks\n", - "xrange_k40 = (600,1000)\n", - "nrm_k40_i = 55\n", - "mean_k40_i = 740\n", - "xrange_tl208 = (1000,1400)\n", - "nrm_tl208_i = 7\n", - "mean_tl208_i = 1210\n", - "hist_bg_fit = hist_bg.Clone()\n", - "hist_bg_fit.GetYaxis().SetRangeUser(0,1e3)\n", - "hist_bg.SetTitle(r\"^{40}K and ^{208}Tl peaks fitting\")\n", - "fit_fn_k40, fit_res_k40 = fit_peak_expbg(hist_bg_fit, name=\"K40_peak\", xl=xrange_k40[0], xr=xrange_k40[1], norm=nrm_k40_i, mean=mean_k40_i, showFit=True)\n", - "fit_fn_tl208, fit_res_tl208 = fit_peak_expbg(hist_bg_fit, name=\"Tl208_peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm_tl208_i, mean=mean_tl208_i, showFit=True)\n", - "\n", - "cv_fit = ROOT.TCanvas(\"cv_fit\", \"cv_fit\", 800,600)\n", - "hist_bg_fit.SetFillStyle(0)\n", - "hist_bg_fit.Draw(\"hist\")\n", - "cv_fit.SetLogy()\n", - "cv_fit.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "3e0906bf-c280-4c5e-b555-871735d01a54", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true, - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Replacing existing TH1: Calibrated background (Potential memory leak).\n", - "Warning in : Replacing existing TH1: Calibrated signal (Potential memory leak).\n" - ] - } - ], - "source": [ - "# Calibrate data and get calibrated histograms\n", - "K40FIT = fit_fn_k40.GetParameter('Mean')\n", - "TL208FIT = fit_fn_tl208.GetParameter('Mean')\n", - "clb_slope = (TL208_MEV - K40_MEV)/(TL208FIT - K40FIT)\n", - "clb_const = K40_MEV - clb_slope * K40FIT\n", - "\n", - "data_bg_clb = acq_bg[ACTIVECH]*clb_slope + clb_const\n", - "data_src_clb = acq_src[ACTIVECH]*clb_slope + clb_const\n", - "\n", - "CLBHISTS_XR = np.max([data_bg_clb.max(), data_src_clb.max()])\n", - "\n", - "hist_bg_clb = ROOT.TH1F(\"Calibrated background\", \"Calibrated Histograms\", BITS9, 0, CLBHISTS_XR)\n", - "hist_src_clb = ROOT.TH1F(\"Calibrated signal\", \"Calibrated Histograms\", BITS9, 0, CLBHISTS_XR)\n", - "\n", - "hist_bg_clb.Fill(data_bg_clb)\n", - "hist_bg_clb.Scale(NORMFACTOR)\n", - "\n", - "hist_src_clb.Fill(data_src_clb)\n", - "\n", - "hist_clean_clb = hist_src_clb.Clone(\"Calibrated signal no BG\")\n", - "hist_clean_clb.Add(hist_bg_clb, -1)\n", - "\n", - "for hist, clr in zip([hist_bg_clb, hist_src_clb, hist_clean_clb], colors_clb):\n", - " hist.GetXaxis().SetTitle(\"Energy (MeV)\")\n", - " hist.GetYaxis().SetTitle(\"Normalized counts\")\n", - " hist.SetLineColor(clr)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "5416087a-578e-4d42-8157-2516db3c6b39", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Error in : Cannot set Y axis to log scale\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot all the histograms\n", - "if ROOT.gROOT.FindObject('cv1'):\n", - " ROOT.gROOT.FindObject('cv1').Close()\n", - "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", - "cv1.Divide(2,1)\n", - "\n", - "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", - "cv1.cd(1)\n", - "hist_bg_clb.SetFillColor(colors_clb[0])\n", - "hist_src_clb.SetFillColorAlpha(colors_clb[1], 0.8)\n", - "lg1.AddEntry(hist_bg_clb, \"Background\", \"l\")\n", - "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", - "hist_bg_clb.Draw(\"hist\")\n", - "hist_src_clb.Draw(\"sames hist\")\n", - "lg1.Draw()\n", - "cv1.cd(1).SetLogy()\n", - "hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", - "\n", - "\n", - "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", - "cv1.cd(2)\n", - "hist_clean_clb.SetFillColor(colors_clb[2])\n", - "hist_clean_clb.Draw(\"hist\")\n", - "cv1.cd(2).SetLogy()\n", - "\n", - "cv1.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "dcbad448-c208-4054-a4ab-e2b71b7ed2a4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 5628.78\n", - "Chi2 = 11257.6\n", - "NDf = 112\n", - "Edm = 1.88824e-07\n", - "NCalls = 124\n", - "Constant = 4002.29 +/- 17.4275 \n", - "Mean = 0.479418 +/- 0.000489701 \n", - "Sigma = 0.136089 +/- 0.000348374 \t (limited)\n" - ] - } - ], - "source": [ - "res = hist_clean_clb.Fit(\"gaus\", \"L S\", \"\", 0, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "6d7f6090-75e8-4779-8a1c-35f1cad0a43d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error in the location of Cs peak: 27.5%\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Deleting canvas with same name: cv_fit\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cv_fit = ROOT.TCanvas(\"cv_fit\", \"cv_fit\", 800,600)\n", - "# hist_bg_fit.SetFillStyle(0)\n", - "hist_clean_clb.Draw(\"hist\")\n", - "hist_clean_clb.SetFillColor(colors_clb[2]-6)\n", - "cv_fit.SetLogy()\n", - "cv_fit.Draw()\n", - "\n", - "\n", - "err = (CS137_MEV - hist_clean_clb.GetFunction(\"gaus\").GetParameter(\"Mean\"))/CS137_MEV\n", - "print(f\"Error in the location of Cs peak: {err*100:.1f}%\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "58f9c3b4-03c0-453a-9d37-cd9cd565ad5f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FWHM=0.32 Energy resolution at 480MeV: 66.84%\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "sigma = 0.136089\n", - "FWHM = np.sqrt(8*np.log(2))*sigma\n", - "print(f\"{FWHM=:.2f} Energy resolution at 480MeV: {FWHM*100/0.479418:.2f}%\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (ROOT in Conda)", - "language": "python", - "name": "comcube" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/energyResolution.ipynb b/noteboks/energyResolution.ipynb deleted file mode 100644 index 9a424b1..0000000 --- a/noteboks/energyResolution.ipynb +++ /dev/null @@ -1,308 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4a3b1bc5-ec73-4d69-a840-07d057f4e2eb", - "metadata": {}, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "project_root=Path(project_root)\n", - "\n", - "from processing import SiphraAcquisition, MetadataLoader\n", - "from analysis import fit_peak_expbg\n", - "\n", - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "CS137_MEV = 0.661\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "75493f51-2089-4f0a-8057-0f2c2bf7466f", - "metadata": {}, - "outputs": [], - "source": [ - "files = sorted((project_root/'data/260323').glob('SUBT_*'))\n", - "acqs = [SiphraAcquisition(file) for file in files]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "5ee51f11-cd24-44c1-bfe5-f0e3b7d8a85c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[SIPHRAAcquisition(File: 'SUBT_01_EnergyResolution_thr30_gain1over100_1over3_Background'),\n", - " SIPHRAAcquisition(File: 'SUBT_02_EnergyResolution_thr30_gain1over100_1over3_Cs137'),\n", - " SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'),\n", - " SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22'),\n", - " SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'),\n", - " SIPHRAAcquisition(File: 'SUBT_06_EnergyResolution_thr30_gain1over100_1over3_Co60'),\n", - " SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'),\n", - " SIPHRAAcquisition(File: 'SUBT_08_EnergyResolution_thr30_gain1over100_1over3_Am241'),\n", - " SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acqs" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "88f4c856-78c0-4ca6-ad58-cc10f3b53649", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cs-137\n", - "Na-22\n", - "Co-60\n", - "Am-241\n", - "{'Cs-137': , 'Cs-137_BG': , 'Cs-137_clean': , 'Na-22': , 'Na-22_BG': , 'Na-22_clean': , 'Co-60': , 'Co-60_BG': , 'Co-60_clean': , 'Am-241': , 'Am-241_BG': , 'Am-241_clean': }\n" - ] - } - ], - "source": [ - "# histograms\n", - "hists = {}\n", - "sources = []\n", - "for sgnl, bg in zip(acqs[1::2], acqs[2::2]):\n", - " filepath = sgnl.filepath.stem\n", - " src = (MetadataLoader.load(sgnl.metadataFile)).source\n", - " sources.append(src)\n", - " print(src)\n", - " # Signal and Background\n", - " hist_sgnl = ROOT.TH1F(f\"{src} Signal\", \"\", BITS12, 0, BITS12)\n", - " hist_bg = ROOT.TH1F(f\"{src} Background\", \"\", BITS12, 0 , BITS12)\n", - " hist_sgnl.Fill(sgnl['s']/len(sgnl.active_chs))\n", - " hist_bg.Fill(bg['s']/len(bg.active_chs))\n", - " hist_bg.Scale(sgnl.exposure/bg.exposure)\n", - " # Clean spectrum\n", - " hist_clean = hist_sgnl.Clone(f\"{src} Clean\")\n", - " hist_clean.Add(hist_bg, -1)\n", - " for hist in [hist_sgnl, hist_bg, hist_clean]:\n", - " # hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Normalized counts\")\n", - " hists[src] = hist_sgnl\n", - " hists[f\"{src}_BG\"] = hist_bg\n", - " hists[f\"{src}_clean\"] = hist_clean\n", - " del hist_sgnl, hist_bg\n", - "print(hists)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "36f654a6-bd64-471f-a0e3-01927db52e3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "Yinit = 0.82 # For stat boxes\n", - "\n", - "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", - "cv.Divide(2,2)\n", - "\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", - "\n", - "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", - " cv.cd(idx+1)\n", - " \n", - " sgnl = hists[src]\n", - " bg = hists[src+'_BG']\n", - " clean = hists[src+'_clean']\n", - " \n", - " lg.AddEntry(sgnl, \"Signal\")\n", - " lg.AddEntry(bg, \"Background\")\n", - " lg.AddEntry(clean, \"Subtracted\")\n", - " sgnl.SetLineColor(colors[0])\n", - " bg.SetLineColor(colors[1])\n", - " clean.SetLineColor(colors[2])\n", - " sgnl.SetTitle(src)\n", - " sgnl.Draw(\"hist\")\n", - " bg.Draw(\"sames hist\")\n", - " clean.Draw(\"sames hist\")\n", - " ROOT.gPad.Update()\n", - " for i, sp in enumerate([sgnl, bg, clean]):\n", - " st = sp.FindObject(\"stats\")\n", - " # print(type(st))\n", - " st.SetY1NDC(Yinit - i*0.08)\n", - " st.SetY2NDC(0.1)\n", - " lg.Draw()\n", - " cv.cd(idx+1).SetLogy()\n", - " cv.Draw()\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "6db7ba78-ddd9-4d76-8bba-bf310f8f1221", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sources\n", - "hists[sources[1]+'_BG']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6b5a5060-fe8f-48ba-af47-c12bca5ea672", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'),\n", - " SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'),\n", - " SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'),\n", - " SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acqs[2::2]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (ROOT in Conda)", - "language": "python", - "name": "comcube" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/etcTests/converters_explore.ipynb b/noteboks/etcTests/converters_explore.ipynb deleted file mode 100644 index 0518f1f..0000000 --- a/noteboks/etcTests/converters_explore.ipynb +++ /dev/null @@ -1,313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "id": "initial_id", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T12:58:03.008075Z", - "start_time": "2026-02-10T12:58:03.004553Z" - }, - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "from pathlib import Path\n", - "\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "66f88d66e88d9206", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:03:05.467415Z", - "start_time": "2026-02-10T13:03:05.464159Z" - } - }, - "outputs": [], - "source": [ - "pr = (Path(os.getcwd())/'..').resolve()\n", - "current_file = os.path.join(os.getcwd(),'converters_explore.ipynb')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3f48f1abf97a9445", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T12:59:11.369712Z", - "start_time": "2026-02-10T12:59:11.364233Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pr.resolve()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ffb7e7ed30a2834e", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:01:53.512177Z", - "start_time": "2026-02-10T13:01:53.507397Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'.ipynb'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p, n = os.path.split(current_file)\n", - "b, e = os.path.splitext(n)\n", - "e" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "85d7ca84600ea5fd", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:21:21.966397Z", - "start_time": "2026-02-10T13:21:21.964395Z" - } - }, - "outputs": [], - "source": [ - "cf = pr/'etcTests/converters_explore.ipynb'" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "2238be3ec8b03b0c", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:21:23.712944Z", - "start_time": "2026-02-10T13:21:23.708626Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/etcTests/converters_explore.ipynb')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cf" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "a92d7f060b8cce59", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:21:25.777143Z", - "start_time": "2026-02-10T13:21:25.772082Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cf.is_file()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "9d4a881e48223aa5", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:30:35.025902Z", - "start_time": "2026-02-10T13:30:35.022787Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n" - ] - } - ], - "source": [ - "for i, e in enumerate([1,2,3,4,5]):\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cd2d503e805701b8", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T13:45:03.620720Z", - "start_time": "2026-02-10T13:45:03.615591Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/etcTests')" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cf.parent" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "739b3aae440091c0", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T14:29:35.397990Z", - "start_time": "2026-02-10T14:29:35.395620Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260204.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260205.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260206.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/260209.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/Untitled.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/Untitled1.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/Untitled2.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/banana.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/histogram_summed.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/spectra_current_thrsh.ipynb'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/noteboks/spectrum_plotter.ipynb')]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(pr.glob(\"*.ipynb\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3d79d86bf3a004d3", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T14:40:11.754644Z", - "start_time": "2026-02-10T14:40:10.738660Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 99.06it/s]\n" - ] - } - ], - "source": [ - "from tqdm import tqdm\n", - "from time import sleep\n", - "\n", - "with tqdm(total=100) as pbar:\n", - " for i in range(10):\n", - " sleep(0.1)\n", - " pbar.update(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "534dadb0-f4ae-48a5-b33f-228b3c072d17", - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/etcTests/siphraAcq_test_json.ipynb b/noteboks/etcTests/siphraAcq_test_json.ipynb deleted file mode 100644 index 3a0a219..0000000 --- a/noteboks/etcTests/siphraAcq_test_json.ipynb +++ /dev/null @@ -1,125 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "083ff4be-8adb-4b9f-955a-bb7c0e8633c8", - "metadata": {}, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent.parent)\n", - "sys.path.append(project_root)\n", - "\n", - "from processing import SiphraAcquisition\n", - "from analysis import fit_peak_expbg" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "afade2b8-2a73-4b6b-ac90-b000e5928d24", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "CS137_MEV = 0.661\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e4fb3482-e47a-44a1-b36f-f4a89dd908b9", - "metadata": {}, - "outputs": [], - "source": [ - "# Datasets \n", - "\n", - "acq1 = SiphraAcquisition(project_root+'/data/260225/SB_1_SiPM_Ch1-4_Ch2_Ch5_8_Ch14_Background.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2532c4ac-3d89-48be-85c4-947e18b92c5b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filepath': PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260225/SB_1_SiPM_Ch1-4_Ch2_Ch5_8_Ch14_Background.csv'),\n", - " 'metadataFile': PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260225/1_SiPM_Ch1-4_Ch2_Ch5_8_Ch14_Background.json'),\n", - " 'active_chs': [2, 14],\n", - " 'exposure': 129.73262667655945,\n", - " 'sipm_chs': ['Ch1-4 to Ch2', 'Ch5-8 to Ch14'],\n", - " 'n_events': 250000,\n", - " 'name': None}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vars(acq1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5ace8361-b67e-4a94-baf7-f40e7658529d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{2: array([200., 109., 125., ..., 55., 49., 59.], shape=(249910,)),\n", - " 14: array([151., 33., 117., ..., 1., 0., 8.], shape=(249910,))}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acq1['a']" - ] - } - ], - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/gainTesting.ipynb b/noteboks/gainTesting.ipynb deleted file mode 100644 index 7aa8f5b..0000000 --- a/noteboks/gainTesting.ipynb +++ /dev/null @@ -1,687 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 66, - "id": "cc81fa7c-d43e-4c17-854d-8bc51e7bdf91", - "metadata": {}, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "import ipynbname\n", - "from pathlib import Path\n", - "\n", - "project_root = str(ipynbname.path().parent.parent)\n", - "sys.path.append(project_root)\n", - "project_root=Path(project_root)\n", - "\n", - "from processing import SiphraAcquisition\n", - "from analysis import fit_peak_expbg" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "f14c60fa-8e5a-4c1b-bd05-6d8f0a23851d", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "BITS12 = 2**12\n", - "BITS9 = 2**9 # 512 typical number of bins used\n", - "# Energy emission peaks in MeV\n", - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "CS137_MEV = 0.661\n", - "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "9d972c1c-3934-4a10-9e7c-4e7b9caa4b3f", - "metadata": {}, - "outputs": [], - "source": [ - "files1 = sorted((project_root/'data/260317').glob('1*.csv'))\n", - "acqs1 = [SiphraAcquisition(file) for file in files1]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "2696fa6e-8416-4df7-9bf9-62d00adcee3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[SIPHRAAcquisition(File: '10_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", - " SIPHRAAcquisition(File: '11_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", - " SIPHRAAcquisition(File: '12_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", - " SIPHRAAcquisition(File: '15_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1'),\n", - " SIPHRAAcquisition(File: '16_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1'),\n", - " SIPHRAAcquisition(File: '17_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1'),\n", - " SIPHRAAcquisition(File: '18_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1')]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acqs1.pop(3)\n", - "acqs1.pop(3)\n", - "acqs1" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "af88b5d2-3d88-4310-ad81-90986efb6ffd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'10'" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acqs1[0].filepath.stem[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "8d9cd696-eb5e-46c8-9673-73e2afc2ebc5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Replacing existing TH1: cmis_gain 1/10 (Potential memory leak).\n", - "Warning in : Replacing existing TH1: cmis_gain 1/100 (Potential memory leak).\n", - "Warning in : Replacing existing TH1: cmis_gain 1/200 (Potential memory leak).\n", - "Warning in : Replacing existing TH1: cmis_gain 1/400 (Potential memory leak).\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", - "lg.SetHeader('CMIS gain')\n", - "\n", - "ci_gain='1V/0.75nC'\n", - "cmis_gains=['1/10', '1/100', '1/200', '1/400']\n", - "hists={}\n", - "\n", - "for idx, (acq, cmsg) in enumerate(zip(acqs1[:4], cmis_gains)):\n", - " hist = ROOT.TH1F(f\"cmis_gain {cmsg}\", f\"{cmsg}\", BITS12, 0, BITS12)\n", - " hist.Fill(acq['s']/len(acq.active_chs))\n", - " hist.Scale(1/acq.exposure)\n", - " # Preeting up ...\n", - " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", - " hist.SetLineColor(colors[idx]+2)\n", - " # legend and storing\n", - " lg.AddEntry(hist, f\"{cmsg}\")\n", - " hists[f\"Acq{acq.filepath.stem[:2]}\"] = hist\n", - " del hist\n", - " hists[f\"Acq{acq.filepath.stem[:2]}\"].Draw('sames hist')\n", - "cv.SetLogy()\n", - "lg.Draw()\n", - "cv.Draw()\n", - "ROOT.gPad.Modified()\n", - "ROOT.gPad.Update()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "65e8172d-a2bc-4306-87b7-66027afc15f4", - "metadata": {}, - "outputs": [], - "source": [ - "# Calibration with acquisition 15(Signal) and 16 (BG)\n", - "files2 = sorted([p for p in (project_root/'data/260317').iterdir() if p.stem[11:13] in ['15', '16']])\n", - "acqs2 = [SiphraAcquisition(file) for file in files2]" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "b831a930-27c1-442f-8dd0-e30f2d8a84a1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260317/SUBTRACTED_15_GainTesting_SiPM_Vbias29,0V_All8Chs_Na22_SIPHRA1.csv'),\n", - " PosixPath('/home/oscar/Projects/SIPHRA_Testing/data/260317/SUBTRACTED_16_GainTesting_SiPM_Vbias29,0V_All8Chs_Background_SIPHRA1.csv')]" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "files2" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "0fcebaeb-0ace-4f57-b151-3a80a51535e9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(acqs2[0].active_chs)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "f6a83f88-b203-4af0-9f0b-18a7b5a0338f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exposures:\n", - " Signal:\t220.216 s\n", - " Background:\t625.392 s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Replacing existing TH1: Signal (Potential memory leak).\n", - "Warning in : Replacing existing TH1: Background (Potential memory leak).\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", - "# lg.SetHeader('')\n", - "\n", - "ci_gain='1V/0.75nC'\n", - "labels=['Signal', 'Background']\n", - "\n", - "ref_exposure = acqs2[0].exposure\n", - "\n", - "for idx, (acq, label) in enumerate(zip(acqs2, labels)):\n", - " hist = ROOT.TH1F(label, label, BITS12, 0, BITS12)\n", - " hist.Fill(acq['s']/len(acq.active_chs))\n", - " # hist.Scale(ref_exposure/acq.exposure)\n", - " # Preeting up ...\n", - " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC, CMIS gain = 1/400\")\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Counts\")\n", - " hist.SetLineColor(colors[idx]+2)\n", - " # legend and storing\n", - " lg.AddEntry(hist, label)\n", - " hists[f\"Acq{acq.filepath.stem[:13]}\"] = hist\n", - " del hist\n", - " hists[f\"Acq{acq.filepath.stem[:13]}\"].Draw('sames hist')\n", - "\n", - "# hist_subt = hists['AcqSUBTRACTED_15'].Clone('Subtracted')\n", - "# hist_subt.Add(hists['AcqSUBTRACTED_16'], -1)\n", - "# hist_subt.Draw('sames hist')\n", - "# hist_subt.SetLineColor(colors[2])\n", - "# lg.AddEntry(hist_subt, 'Subtracted')\n", - "\n", - "cv.SetLogy()\n", - "lg.Draw()\n", - "cv.Draw()\n", - "ROOT.gPad.Modified()\n", - "ROOT.gPad.Update()\n", - "print(f\"Exposures:\\n Signal:\\t{acqs2[0].exposure:>.3f} s\\n Background:\\t{acqs2[1].exposure:>.3f} s\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "05919cfa-b075-4898-8f7b-56039d4c8da1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Acq10': ,\n", - " 'Acq11': ,\n", - " 'Acq12': ,\n", - " 'Acq15': ,\n", - " 'AcqSUBTRACTED_10': ,\n", - " 'AcqSUBTRACTED_11': ,\n", - " 'AcqSUBTRACTED_16': ,\n", - " 'AcqSUBTRACTED_15': }" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hists" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "8e7a42e8-c3a4-451c-b635-5d37c1167faf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 7605.91\n", - "Chi2 = 15211.8\n", - "NDf = 197\n", - "Edm = 1.10191e-06\n", - "NCalls = 138\n", - "Constant = 1267.88 +/- 5.7826 \n", - "Mean = 172.076 +/- 0.0854468 \n", - "Sigma = 22.9615 +/- 0.0616869 \t (limited)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "files2 = sorted((project_root/'data/260317').glob('SUBTRACTED_*'))\n", - "acqs2 = [SiphraAcquisition(file) for file in files2]\n", - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", - "\n", - "hist = hist_subt.Clone('hist_subt_calib')\n", - "hist.SetTitle(r'^{22}Na subtracted spectrum, CI gain = 1/0.75 pC, CMIS gain = 1/400')\n", - "fit1 = hist.Fit('gaus', 'L S', \"\", 100, 300)\n", - "hist.Draw()\n", - "cv.SetLogy()\n", - "cv.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "daf8a1de-c392-4879-a4a9-619db60f669e", - "metadata": {}, - "outputs": [], - "source": [ - "# Calibration with acquisition 15(Signal) and 16 (BG)\n", - "files2 = sorted((project_root/'data/260317').glob('SUBTRACTED_*'))\n", - "acqs2 = [SiphraAcquisition(file) for file in files2]\n", - "if ROOT.gROOT.FindObject('cv'):\n", - " ROOT.gROOT.FindObject('cv').Close()\n", - "\n", - "cv = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.16)\n", - "\n", - "lg = ROOT.TLegend(0.58, 0.61, 0.75, 0.83)\n", - "# lg.SetHeader('CMIS gain')\n", - "\n", - "ci_gain='1V/0.75nC'\n", - "labels=['Signal', 'Background']\n", - "hists={}\n", - "\n", - "for idx, (acq, label) in enumerate(zip(acqs2, labels)):\n", - " hist = ROOT.TH1F(label, label, BITS12, 0, BITS12)\n", - " hist.Fill(acq['s']/len(acq.active_chs))\n", - " hist.Scale(1/acq.exposure)\n", - " # Preeting up ...\n", - " hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC, CMIS gain = 1/400\")\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", - " hist.SetLineColor(colors[idx]+2)\n", - " # legend and storing\n", - " lg.AddEntry(hist, label)\n", - " hists[f\"Acq{acq.filepath.stem[:13]}\"] = hist\n", - " del hist\n", - " hists[f\"Acq{acq.filepath.stem[:13]}\"].Draw('sames hist')\n", - "\n", - "\n", - "\n", - "cv.SetLogy()\n", - "lg.Draw()\n", - "cv.Draw()\n", - "ROOT.gPad.Modified()\n", - "ROOT.gPad.Update()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "2028d39f-0e60-4bbe-b3c9-55cbbc63b14b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2004" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2278 - 274" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "f3576c1a-50d1-4778-920f-1dd754dd473d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1204" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1841 - 637" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "a74af979-2cdc-4da3-8164-59ab0a3be28d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "560" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "984-424" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (ROOT in Conda)", - "language": "python", - "name": "comcube" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/histogram_summed.ipynb b/noteboks/histogram_summed.ipynb deleted file mode 100644 index 7c025d8..0000000 --- a/noteboks/histogram_summed.ipynb +++ /dev/null @@ -1,294 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d476cf44-4356-49a8-890b-c65fbd833335", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "218b623e-3fa9-4146-b944-a9df401a72e7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
058221386028829642069149150293103...12914012411812027810813522309
158321400280829642069149150223103...129140123117120236106136142192
258420400280829642069149150254104...12914012311812119010913622189
358520400280829642069149149307105...128140122119120325108135142372
458621445339629642069149151308105...13014012311712028310813722332
\n", - "

5 rows × 25 columns

\n", - "
" - ], - "text/plain": [ - " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 Ch2 Ch3 \\\n", - "0 5 82 21 3860288 296 42069 149 150 293 103 \n", - "1 5 83 21 4002808 296 42069 149 150 223 103 \n", - "2 5 84 20 4002808 296 42069 149 150 254 104 \n", - "3 5 85 20 4002808 296 42069 149 149 307 105 \n", - "4 5 86 21 4453396 296 42069 149 151 308 105 \n", - "\n", - " ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax Summed \n", - "0 ... 129 140 124 118 120 278 108 135 2 2309 \n", - "1 ... 129 140 123 117 120 236 106 136 14 2192 \n", - "2 ... 129 140 123 118 121 190 109 136 2 2189 \n", - "3 ... 128 140 122 119 120 325 108 135 14 2372 \n", - "4 ... 130 140 123 117 120 283 108 137 2 2332 \n", - "\n", - "[5 rows x 25 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "df = pd.read_csv('../data/260123/3_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Background_attempt2.28.5V.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6e72aec1-8f58-4b31-9ee7-058b813041ad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "N_BINS = 512\n", - "BITS_12 = 2**12\n", - "\n", - "summed_spectrum = df['Summed'].tolist()\n", - "plt.figure(figsize=(14,10), dpi=80)\n", - "plt.hist(summed_spectrum, N_BINS, range=(0, 4*BITS_12), log=True, histtype='step')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1c42a169-327e-42ed-a3c4-97a7390bbe36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4096" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2**12" - ] - } - ], - "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", - 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"2026-02-09T15:16:21.512166Z" - } - }, - "source": [ - "import ROOT\n", - "import numpy as np\n", - "\n", - "c1 = ROOT.TCanvas(\"c1\",\"hists with different scales\",600,400)\n", - "\n", - "ROOT.gStyle.SetOptStat(False)\n", - "\n", - "h1 = ROOT.TH1F(\"h1\",\"my histogram\",100,-3,3)\n", - "\n", - "h1.Fill(np.array([ROOT.gRandom.Gaus(0, 1) for _ in range(10000)]))\n", - "\n", - "h1.Draw()\n", - "c1.Update()\n", - "\n", - "hint1 = ROOT.TH1F(\"hint1\",\"h1 bins integral\",100,-3,3)\n", - "\n", - "sum = 0\n", - "for i in range(1,101) :\n", - " sum += h1.GetBinContent(i)\n", - " hint1.SetBinContent(i,sum)\n", - "\n", - "rightmax = 1.1*hint1.GetMaximum()\n", - "scale = ROOT.gPad.GetUymax()/rightmax\n", - "hint1.SetLineColor(\"kRed\")\n", - "hint1.Scale(scale)\n", - "hint1.Draw(\"same\")\n", - "\n", - "axis = ROOT.TGaxis(ROOT.gPad.GetUxmax(),ROOT.gPad.GetUymin(),\n", - " ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymax(),0,rightmax,510,\"+L\")\n", - "axis.SetLineColor(\"kRed\")\n", - "axis.SetLabelColor(\"kRed\")\n", - "axis.Draw()" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "\n", - "
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DetectorIDTriggerTime_subTime_secTime_gpsTempCh1Ch2Ch3...Ch9Ch10Ch11Ch12Ch13Ch14Ch15Ch16ArgmaxSummed
0560000121595446384242069149151614106...13014112411812152711013722893
1560000221610680384242069149150621104...131140125118122689110137143062
2560000321620254384242069149150482105...13014112211812037210813622597
3560000421630512384242069149151476104...13014112311812040510913722629
4560000521638502384242069149150799106...12914112511812155110913723104
..................................................................
9967356999952010745499428742069149151915106...131142124118122981109137143657
9967456999962111026960428742069149151900105...13114012411912166610813823320
99675569999720110269604287420691491521074105...13114212511912195711013723790
9967656999982011026960428742069149150975106...13014112311912169810913723421
9967756999992111598563428742069149149935104...12914012411812072710813523403
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99678 rows × 25 columns

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" - ], - "text/plain": [ - " Detector ID Trigger Time_sub Time_sec Time_gps Temp Ch1 \\\n", - "0 5 600001 21 595446 3842 42069 149 151 \n", - "1 5 600002 21 610680 3842 42069 149 150 \n", - "2 5 600003 21 620254 3842 42069 149 150 \n", - "3 5 600004 21 630512 3842 42069 149 151 \n", - "4 5 600005 21 638502 3842 42069 149 150 \n", - "... ... ... ... ... ... ... ... ... \n", - "99673 5 699995 20 10745499 4287 42069 149 151 \n", - "99674 5 699996 21 11026960 4287 42069 149 151 \n", - "99675 5 699997 20 11026960 4287 42069 149 152 \n", - "99676 5 699998 20 11026960 4287 42069 149 150 \n", - "99677 5 699999 21 11598563 4287 42069 149 149 \n", - "\n", - " Ch2 Ch3 ... Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16 Argmax \\\n", - "0 614 106 ... 130 141 124 118 121 527 110 137 2 \n", - "1 621 104 ... 131 140 125 118 122 689 110 137 14 \n", - "2 482 105 ... 130 141 122 118 120 372 108 136 2 \n", - "3 476 104 ... 130 141 123 118 120 405 109 137 2 \n", - "4 799 106 ... 129 141 125 118 121 551 109 137 2 \n", - "... ... ... ... ... ... ... ... ... ... ... ... ... \n", - "99673 915 106 ... 131 142 124 118 122 981 109 137 14 \n", - "99674 900 105 ... 131 140 124 119 121 666 108 138 2 \n", - "99675 1074 105 ... 131 142 125 119 121 957 110 137 2 \n", - "99676 975 106 ... 130 141 123 119 121 698 109 137 2 \n", - "99677 935 104 ... 129 140 124 118 120 727 108 135 2 \n", - "\n", - " Summed \n", - "0 2893 \n", - "1 3062 \n", - "2 2597 \n", - "3 2629 \n", - "4 3104 \n", - "... ... \n", - "99673 3657 \n", - "99674 3320 \n", - "99675 3790 \n", - "99676 3421 \n", - "99677 3403 \n", - "\n", - "[99678 rows x 25 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "dfs = []\n", - "dfs.append(pd.read_csv('../data/260127/6_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold10.csv'))\n", - "dfs.append(pd.read_csv('../data/260127/7_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold20.csv'))\n", - "dfs.append(pd.read_csv('../data/260127/8_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold5.csv'))\n", - "dfs.append(pd.read_csv('../data/260127/9_SiPM_PrernaMark_Ch1-4_Ch2_Ch5-8_Ch14_Cs137_28.5V_threshold15.csv'))\n", - "\n", - "dfs[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d8be8d5a-6d8e-4666-bace-5874e4a1c711", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "N_BINS = 512\n", - "BITS_12 = 2**12\n", - "\n", - "summed_spectra = [df['Summed'].tolist() for df in dfs]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(14,14), dpi=80)\n", - "legend = ['Thrsh = 10', 'Thrsh = 20', 'Thrsh = 5', 'Thrsh = 15']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(0, 4*BITS_12), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel('Channel')\n", - "plt.ylabel('Number of events')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dd891565-4321-4120-b738-8a31ccd64ed4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [31.708, 893.006, 24.152, 335.703]\n", - "plt.figure(figsize=(14,10), dpi=80)\n", - "legend = ['Thrsh = 10', 'Thrsh = 20', 'Thrsh = 5', 'Thrsh = 15']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(0, 4*BITS_12), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel('Channel')\n", - "plt.ylabel('Rate of events')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6cd2bea1-15c6-4863-b29a-2fd86584959d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "times = [31.708, 893.006, 24.152, 335.703]\n", - "plt.figure(figsize=(14,10), dpi=80)\n", - "legend = ['Thrsh = 10', 'Thrsh = 20', 'Thrsh = 5', 'Thrsh = 15']\n", - "for idx,s in enumerate(summed_spectra):\n", - " plt.hist(s, N_BINS, range=(2000, 10000), weights=(1/times[idx])*np.ones_like(s), log=True, histtype='step', label=legend[idx])\n", - "plt.legend()\n", - "plt.xlabel('Channel')\n", - "plt.ylabel('Rate of events')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9bcebd34-62ae-4fb7-aa41-23df1379432a", - "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": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noteboks/spectrum_plotter.ipynb b/noteboks/spectrum_plotter.ipynb deleted file mode 100644 index fdd784b..0000000 --- a/noteboks/spectrum_plotter.ipynb +++ /dev/null @@ -1,1022 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:37:32.276396Z", - "start_time": "2026-02-10T09:37:32.169808Z" - } - }, - "outputs": [], - "source": [ - "import ROOT\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(),\"..\"))\n", - "sys.path.append(project_root)\n", - "\n", - "# ROOT.gROOT.SetBatch(False)\n", - "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", - "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", - "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", - "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "44c9a6d0-0e35-49fd-af28-5fa39de26322", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "18.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "180/5/2\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "68a9cd08dc972f65", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:37:35.457562Z", - "start_time": "2026-02-10T09:37:33.669215Z" - } - }, - "outputs": [], - "source": [ - "BITS_12 = 2**12\n", - "N_BINS = 512\n", - "\n", - "dfs_SiPM58_BG = []\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/1_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM58_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "times_SiPM58_BG = [119.634, 161.062, 148.920, 164.828]\n", - "\n", - "dfs_SiPM14_BG = []\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/5_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Trh1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/2_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/3_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "dfs_SiPM14_BG.append(pd.read_csv('../data/260209/4_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Background.csv'))\n", - "times_SiPM14_BG = [34.836, 80.792, 54.386, 90.837]\n", - "\n", - "dfs_SiPM14_Cs137 = []\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/12_SiPM_ChannelsTest_Ch1-4_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/11_SiPM_ChannelsTest_Ch1-4_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/10_SiPM_ChannelsTest_Ch1-4_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM14_Cs137.append(pd.read_csv('../data/260209/9_SiPM_ChannelsTest_Ch1-4_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "times_SiPM14_Cs137 = [23.381, 24.606, 24.144, 24.645]\n", - "\n", - "dfs_SiPM58_Cs137 = []\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/13_SiPM_ChannelsTest_Ch5-8_Ch2_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/14_SiPM_ChannelsTest_Ch5-8_Ch6_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/15_SiPM_ChannelsTest_Ch5-8_Ch10_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "dfs_SiPM58_Cs137.append(pd.read_csv('../data/260209/16_SiPM_ChannelsTest_Ch5-8_Ch14_CT_Thr1_cmisvoffset127_Cs137.csv'))\n", - "times_SiPM58_Cs137 = [24.720, 24.916, 24.848, 24.934]\n", - "\n", - "SIPHRA_Ch_list = ['Ch2', 'Ch6', 'Ch10', 'Ch14']\n", - "dfs = [dfs_SiPM14_BG, dfs_SiPM14_Cs137, dfs_SiPM58_BG, dfs_SiPM58_Cs137]\n", - "data_SiPM_chs = ['1-4', '1-4', '5-8', '5-8']\n", - "legend = ['Ch.2', 'Ch.6', 'Ch.10', 'Ch.14']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cae21a3ee684d69f", - "metadata": { - "ExecuteTime": { - "end_time": "2026-02-10T09:43:25.401070Z", - "start_time": "2026-02-10T09:43:24.995723Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Name: stats Title: A special TPaveText to draw histogram statistics.\n", - "Name: stats Title: A special TPaveText to draw histogram statistics.\n", - "Name: stats Title: A special TPaveText to draw histogram statistics.\n", - "Name: stats Title: A special TPaveText to draw histogram statistics.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if ROOT.gROOT.FindObject(\"c1\"):\n", - " ROOT.gROOT.FindObject(\"c1\").Close()\n", - "\n", - "c1 = ROOT.TCanvas(\"c1\", \"c1\", 800, 600)\n", - "ROOT.gStyle.SetOptStat(11)\n", - "ROOT.gStyle.SetStatFontSize(0.03)\n", - "ROOT.gStyle.SetStatW(0.15)\n", - "\n", - "IamLegend = ROOT.TLegend(0.65, 0.7, 0.8, 0.88)\n", - "\n", - "ch_spectra = []\n", - "frame_lst = dfs_SiPM14_BG\n", - "colors = [ROOT.kBlue, ROOT.kRed, ROOT.kGreen, ROOT.kOrange]\n", - "for df, ch, lg, t, c in zip(frame_lst, SIPHRA_Ch_list, legend, times_SiPM14_BG, colors):\n", - " hist = ROOT.TH1F(ch, \"Background spectra SiPM Ch. 1-4\", N_BINS, 0, BITS_12)\n", - " for dp in df[ch]:\n", - " hist.Fill(dp)\n", - " hist.Scale(1/t)\n", - " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", - " hist.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", - " hist.SetLineColor(c-2)\n", - " st=hist.FindObject(\"stats\")\n", - " # st.SetX1NDC(0.65)\n", - " IamLegend.SetHeader('SIPHRA channel')\n", - " IamLegend.AddEntry(hist, lg, 'l')\n", - " ch_spectra.append(hist)\n", - "\n", - "ch_spectra[0].Draw(\"hist\")\n", - "for sp in ch_spectra[1:]:\n", - " sp.Draw(\"hist,sames\")\n", - "c1.SetLogy()\n", - "IamLegend.Draw()\n", - "\n", - "ROOT.gPad.Update()\n", - "Yinit = 0.82\n", - "for i, sp in enumerate(ch_spectra):\n", - " st = sp.FindObject(\"stats\")\n", - " print(st)\n", - " st.SetY1NDC(Yinit - i*0.08)\n", - " st.SetY2NDC(0.1)\n", - " \n", - "\n", - "c1.Draw()\n", - "ROOT.gPad.Modified()\n", - "ROOT.gPad.Update()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b9c56acf-bfdb-4895-aa41-7f8c6b7ff2ac", - "metadata": {}, - "outputs": [], - "source": [ - "# def gauspeak(n_init):\n", - "# return f\"[{n_init}]*TMath::Gaus(x, [{n_init+1}], [{n_init+2}]\"\n", - "\n", - "# formula = \"[0]*TMath::Exp(-x/[1]) + [2]*TMath::Gaus(x, [3], [4])\"\n", - "\n", - "# c2 = ROOT.TCanvas(\"c2\", \"c2\", 800, 600)\n", - "# # bg_exp = ROOT.TF1(\"ExpDecayBG\", \"[p0]*TMath::Exp(x*[p1]/[p2])\", 0, BITS_12)\n", - "# # gaussian1 = ROOT.TF1(\"K40peak\", \"TMath::Gaus(x, [mean], [sigma])\", 800, 1100)\n", - "# # gaussian2 = ROOT.TF1(\"Tl208peak\", \"TMath::Gaus(x, [mean], [sigma])\", 1300, 1600)\n", - "\n", - "# bg = ROOT.TF1(\"ExpDecayBG\", formula, 700, 1200, 5)\n", - "# bg.SetParNames(\"exp_fact\", \"exp_denom\", \"norm\", \"mean\", \"sigma\")\n", - "# bg.SetParameters(180, 200, 4, 904, 4)\n", - "\n", - "# fitResult = ch_spectra[2].Fit(bg, \"L S\", \"\", 700, 1200)\n", - "# ch_spectra[2].GetXaxis().SetRangeUser(0, BITS_12)\n", - "# ch_spectra[2].Draw(\"SAME hist\")\n", - "# c2.SetLogy()\n", - "# c2.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e8495329-2fcb-47eb-bb43-ad3f00010c74", - "metadata": {}, - "outputs": [], - "source": [ - "# def gauspeak(n_init):\n", - "# return f\"[{n_init}]*TMath::Gaus(x, [{n_init+1}], [{n_init+2}])\"\n", - "\n", - "# def bg_exp(n_init):\n", - "# return f\"[{n_init}]*TMath::Exp(-x/[{n_init+1}])\"\n", - "# norm = bg.GetParameter(2)\n", - "# mean = bg.GetParameter(3)\n", - "# sigma = bg.GetParameter(4)\n", - "# const = bg.GetParameter(0)\n", - "# expdenom = bg.GetParameter(1)\n", - "# c3 = ROOT.TCanvas(\"c3\", \"c3\", 800, 600)\n", - "# peak = ROOT.TF1(\"K40_peak\", gauspeak(0), 400, 1500, 3)\n", - "# peak.SetParameters(norm, mean, sigma)\n", - "# peak.SetLineColor(ROOT.kMagenta)\n", - "# onlybg = ROOT.TF1(\"onlybg\", \"[0]*TMath::Exp(-x/[1])\", 100, 2000, 2)\n", - "# onlybg.SetParameters(const, expdenom)\n", - "# onlybg.SetLineColor(ROOT.kBlue)\n", - "# onlybg.SetLineStyle(2)\n", - "# ch_spectra[2].Draw(\"SAME hist\")\n", - "# peak.Draw(\"same\")\n", - "# onlybg.Draw(\"same\")\n", - "# c3.SetLogy()\n", - "# c3.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f7ae8560-be45-46f7-b37f-aa423cd53cc7", - "metadata": {}, - "outputs": [], - "source": [ - "def gauspeak(n_init):\n", - " return f\"[{n_init}]*TMath::Gaus(x, [{n_init+1}], [{n_init+2}])\"\n", - "\n", - "def bg_exp(n_init):\n", - " return f\"[{n_init}]*TMath::Exp(-x/[{n_init+1}])\"\n", - "\n", - "def peak_and_bg():\n", - " return f\"{bg_exp(0)}+{gauspeak(2)}\"\n", - "\n", - "def fitpeak(hsit, name, xl, xr, norm, mean, sigma=70, const=200, denom=200, showFit=False, keep_prev_fncs=True):\n", - " fit_fn = ROOT.TF1(name, peak_and_bg(), xl, xr, 5)\n", - " fit_fn.SetParNames(\"Const\", \"Denom\", \"Norm\", \"Mean\", \"Sigma\")\n", - " fit_fn.SetParameters(const, denom, norm, mean, sigma)\n", - " options = \"L S\" if showFit else \"0 S\"\n", - " options += \" +\" if keep_prev_fncs else \"\"\n", - " fit_result = hist.Fit(fit_fn, options, \"\", xl, xr)\n", - " return fit_fn, fit_result\n", - "\n", - "\n", - "# fit_fn_ch14, fit_ch14 = fitpeak(ch_spectra[3], name=\"K40_peak\", xl=650, xr=1200, norm=4, mean=840)\n", - "\n", - "# c4 = ROOT.TCanvas(\"c4\", \"c4\", 800, 600)\n", - "# ch_spectra[3].GetXaxis().SetRangeUser(0, BITS_12)\n", - "# # ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", - "# ch_spectra[3].Draw(\"hist\")\n", - "# c4.SetLogy()\n", - "# c4.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "77b328fc-c345-46fa-a780-8354a292c5e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.866612\n", - "Chi2 = 1.73322\n", - "NDf = 64\n", - "Edm = 1.48724e-06\n", - "NCalls = 1442\n", - "Const = 22.546 +/- 26.3142 \n", - "Denom = 234.686 +/- 86.4285 \n", - "Norm = 0.634481 +/- 0.336032 \n", - "Mean = 894.211 +/- 40.4785 \n", - "Sigma = -68.8513 +/- 58.3939 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.573121\n", - "Chi2 = 1.14624\n", - "NDf = 64\n", - "Edm = 4.3817e-07\n", - "NCalls = 204\n", - "Const = 120.222 +/- 75.8598 \n", - "Denom = 209.018 +/- 33.8978 \n", - "Norm = 2.09451 +/- 0.638154 \n", - "Mean = 858.191 +/- 28.3061 \n", - "Sigma = 73.0178 +/- 29.7389 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.778431\n", - "Chi2 = 1.55686\n", - "NDf = 64\n", - "Edm = 1.3971e-07\n", - "NCalls = 191\n", - "Const = 178.751 +/- 108.57 \n", - "Denom = 205.251 +/- 35.1254 \n", - "Norm = 2.78572 +/- 0.7156 \n", - "Mean = 888.101 +/- 28.8723 \n", - "Sigma = 85.669 +/- 31.3128 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.486844\n", - "Chi2 = 0.973688\n", - "NDf = 64\n", - "Edm = 1.03434e-06\n", - "NCalls = 224\n", - "Const = 93.493 +/- 63.0911 \n", - "Denom = 216.779 +/- 38.4718 \n", - "Norm = 1.98405 +/- 0.605315 \n", - "Mean = 859.999 +/- 29.857 \n", - "Sigma = 75.4189 +/- 30.6475 \n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k40peak_init = 840\n", - "xrange_k40 = (650, 1200)\n", - "norms_init = [1,3,4,3]\n", - "sig_init = 70\n", - "\n", - "fit_fns = []\n", - "k40peaks_means = []\n", - "k40peaks_sigmas = []\n", - "\n", - "for hist, nrm, ch in zip(ch_spectra, norms_init, legend):\n", - " fit_fn, fit_res = fitpeak(hist, name=\"K40_peak\", xl=xrange_k40[0], xr=xrange_k40[1], norm=nrm, mean=k40peak_init, showFit=True)\n", - " fit_fns.append(fit_fn)\n", - " k40peaks_means.append(fit_fns[-1].GetParameter(3))\n", - " k40peaks_sigmas.append(fit_fns[-1].GetParameter(4))\n", - "\n", - "c5 = ROOT.TCanvas(\"c4\", \"c4\", 800, 600)\n", - "# ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", - "\n", - "for sp in ch_spectra:\n", - " sp.Draw(\"hist,sames\")\n", - "c5.SetLogy()\n", - "c5.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0eaac70e-3547-4a25-89cc-cb3aea4d82cf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.948685\n", - "Chi2 = 1.89737\n", - "NDf = 83\n", - "Edm = 8.42893e-06\n", - "NCalls = 628\n", - "Const = 396.93 +/- 3632.12 \n", - "Denom = 148.287 +/- 179.97 \n", - "Norm = 0.0896652 +/- 0.0935118 \n", - "Mean = 1443.17 +/- 191.88 \n", - "Sigma = 104.392 +/- 145.365 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.560084\n", - "Chi2 = 1.12017\n", - "NDf = 83\n", - "Edm = 4.59517e-06\n", - "NCalls = 634\n", - "Const = 4562.96 +/- 35201.8 \n", - "Denom = 123.626 +/- 106.668 \n", - "Norm = 0.387189 +/- 0.194726 \n", - "Mean = 1362.9 +/- 69.808 \n", - "Sigma = 107.599 +/- 58.5199 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.924977\n", - "Chi2 = 1.84995\n", - "NDf = 83\n", - "Edm = 4.84348e-06\n", - "NCalls = 486\n", - "Const = 1842.28 +/- 10112 \n", - "Denom = 144.401 +/- 103.684 \n", - "Norm = 0.477086 +/- 0.233785 \n", - "Mean = 1390.77 +/- 73.4451 \n", - "Sigma = 115.419 +/- 61.6398 \n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "MinFCN = 0.629655\n", - "Chi2 = 1.25931\n", - "NDf = 83\n", - "Edm = 1.28102e-06\n", - "NCalls = 756\n", - "Const = 6832.78 +/- 72864.8 \n", - "Denom = 117.513 +/- 134.211 \n", - "Norm = 0.336016 +/- 0.216213 \n", - "Mean = 1353.79 +/- 96.0207 \n", - "Sigma = 114.096 +/- 72.613 \n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tl208peak_init = 1380\n", - "xrange_tl208 = (1100, 1800)\n", - "norms_init = [0.1, 0.4, 0.4, 0.5]\n", - "sig_init = 70\n", - "\n", - "fit_fns = []\n", - "tl208peaks_means = []\n", - "tl208peaks_sigmas = []\n", - "\n", - "for hist, nrm, ch in zip(ch_spectra, norms_init, legend):\n", - " fit_fn, fit_res = fitpeak(hist, name=\"Tl208peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm, mean=tl208peak_init, showFit=True)\n", - " fit_fns.append(fit_fn)\n", - " tl208peaks_means.append(fit_fns[-1].GetParameter(3))\n", - " tl208peaks_sigmas.append(fit_fns[-1].GetParameter(4))\n", - "\n", - "c5 = ROOT.TCanvas(\"c5\", \"c5\", 800, 600)\n", - "# ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", - "\n", - "for sp in ch_spectra:\n", - " sp.Draw(\"hist,sames\")\n", - "c5.SetLogy()\n", - "c5.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5669d26f-4957-487c-bbd7-6c2f43b9a298", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Deleting canvas with same name: c5\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c5 = ROOT.TCanvas(\"c5\", \"c5\", 800, 600)\n", - "# ch_spectra[3].GetListOfFunctions().Add(\"K40_peak\")\n", - "t = []\n", - "for sp in ch_spectra:\n", - " sp.Draw(\"hist,sames\")\n", - " tl208_fn = sp.GetFunction(\"Tl208peak\")\n", - " text_x = tl208_fn.GetParameter(3)\n", - " text_y = tl208_fn.Eval(text_x)\n", - " t.append(ROOT.TText(text_x, text_y,f\"{text_x:.2f}\"))\n", - " t[-1].SetTextSize(10)\n", - " t[-1].SetTextAngle(90)\n", - " t[-1].Draw()\n", - " # sp.GetFunction(\"Tl208peak\").Delete()\n", - "for sp in ch_spectra:\n", - " k40_fn = sp.GetFunction(\"K40_peak\")\n", - " text_x = k40_fn.GetParameter(3)\n", - " text_y = k40_fn.Eval(text_x)\n", - " t.append(ROOT.TText(text_x, text_y,f\"{text_x:.2f}\"))\n", - " t[-1].SetTextSize(10)\n", - " t[-1].SetTextAngle(90)\n", - " t[-1].Draw()\n", - "\n", - "IamLegend.Draw()\n", - "c5.SetLogy()\n", - "c5.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f85ab3b2-072e-4d81-b68f-96534fbe5129", - "metadata": {}, - "outputs": [], - "source": [ - "#def get_peak_from_fit(fit_fn, " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cd49a392-2942-4c17-8741-aae09b231840", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Calibration: E = 0.0023 * Ch + -0.5498\n", - "\n", - "Max. Energy: 9.02 MeV\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "K40_MEV = 1.460\n", - "TL208_MEV = 2.614\n", - "\n", - "raw_data = frame_lst[3][SIPHRA_Ch_list[3]].to_numpy()\n", - "\n", - "calib_m = (TL208_MEV - K40_MEV) / (tl208peaks_means[3] - k40peaks_means[3])\n", - "calib_b = K40_MEV - calib_m*k40peaks_means[3]\n", - "print(f\"\\nCalibration: E = {calib_m:.4f} * Ch + {calib_b:.4f}\\n\")\n", - "\n", - "calib_data = (raw_data * calib_m + calib_b)\n", - "print(f\"Max. Energy: {BITS_12*calib_m+calib_b:.2f} MeV\")\n", - "\n", - "hist_calib = ROOT.TH1F(\"Ch14_BG_calib\", \"Spectrum calibrated w.r.t. K40 & Tl208\", N_BINS, min(calib_data), max(calib_data))\n", - "\n", - "hist_calib.Fill(calib_data)\n", - "hist_calib.Scale(1/times_SiPM14_BG[3])\n", - "hist_calib.SetLineColor(colors[3]-3)\n", - "\n", - "c = ROOT.TCanvas(\"c\", \"c\", 800, 600)\n", - "hist_calib.Draw(\"hist\")\n", - "c.SetLogy()\n", - "c.Draw()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a104c522-b356-4966-9ef4-6175f5c94926", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************************************\n", - " Invalid FitResult (status = 2 )\n", - "****************************************\n", - "Minimizer is Minuit2 / Migrad\n", - "Chi2 = 0\n", - "NDf = 0\n", - "Edm = 1.06209e-16\n", - "NCalls = 89\n", - "Const = 200 +/- 0 \n", - "Denom = 0.1 +/- 0 \n", - "Norm = -103.981 +/- 0 \n", - "Mean = 0.66 +/- 0 \n", - "Sigma = 0.5 +/- 0 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning in : Abnormal termination of minimization.\n", - "Warning in : Deleting canvas with same name: c\n", - "Error in : Cannot set Y axis to log scale\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "source_raw_dat = dfs_SiPM14_Cs137[3][SIPHRA_Ch_list[3]].to_numpy()\n", - "source_calib_data = source_raw_dat*calib_m + calib_b\n", - "source_hist_calib = ROOT.TH1F(\"Ch14_Cs137_calib\", \"\", N_BINS, min(source_calib_data), max(source_calib_data))\n", - "source_hist_calib.Fill(source_calib_data)\n", - "source_hist_calib.Scale(1/times_SiPM14_Cs137[3])\n", - "\n", - "fitpeak(source_hist_calib, name=\"Cs137peak\", xl=0.2, xr=1.2, norm=170, mean=0.66, sigma=0.5, const=200, denom=0.1, showFit=True)\n", - "\n", - "hist_calib.GetYaxis().SetRangeUser(0,2e2)\n", - "\n", - "c = ROOT.TCanvas(\"c\", \"c\", 800, 600)\n", - "source_hist_calib.Draw(\"hist same\")\n", - "source_hist_calib.GetXaxis().SetTitle(\"Energy (MeV)\")\n", - "source_hist_calib.GetYaxis().SetTitle(r\"Count Rate (s^{-1})\")\n", - "hist_calib.Draw(\"hist same\")\n", - "hist_calib.SetFillColorAlpha(colors[3]-3, 0.5)\n", - "source_hist_calib.SetFillColor(ROOT.kBlue)\n", - "\n", - "c.SetLogy()\n", - "c.Draw()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb5c869d-50a5-4acc-a2c3-d26e9b61ee59", - "metadata": {}, - "outputs": [], - "source": [ - "fit_fn, fit_res = fitpeak(hist, name=\"Tl208peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm, mean=tl208peak_init, showFit=True)\n", - " fit_fns.append(fit_fn)\n", - " tl208peaks_means.append(fit_fns[-1].GetParameter(3))\n", - " tl208peaks_sigmas.append(fit_fns[-1].GetParameter(4))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (ROOT in Conda)", - "language": "python", - "name": "comcube" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/processing/__pycache__/metadata.cpython-314.pyc b/processing/__pycache__/metadata.cpython-314.pyc index d26f49e765a4ea101e38cddc11e3315eb9d2e7ef..d45472e2c3619de724e58c62a7c2a2b459fd6bdf 100644 GIT binary patch delta 19 ZcmdlhwpWZxn~#@^0SFkUZ{#xO1^_6J1Cjs$ delta 19 ZcmdlhwpWZxn~#@^0SHX diff --git a/siphractl/ComptonCam_DMA_To_RAW_File/Makefile b/siphractl/ComptonCam_DMA_To_RAW_File/Makefile deleted file mode 100644 index 300b533..0000000 --- a/siphractl/ComptonCam_DMA_To_RAW_File/Makefile +++ /dev/null @@ -1,17 +0,0 @@ -# Read DMA datas and write them in a file - -CFLAGS = -g -Wall -Wextra -Wpedantic -pedantic -lhiredis - -MAIN = dma_to_raw_file - -all: $(MAIN) - -$(MAIN): src/dma_to_raw_file.c - $(CC) $^ -o $@ $(CFLAGS) - -clean: - find . -name "*.o" -delete - find . -name "*~" -delete - -distclean: clean - $(RM) -rf $(MAIN) diff --git a/siphractl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c b/siphractl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c deleted file mode 100644 index 96c7319..0000000 --- a/siphractl/ComptonCam_DMA_To_RAW_File/src/dma_to_raw_file.c +++ /dev/null @@ -1,437 +0,0 @@ -/** - * @file dma_to_raw_file.c - * - * @date 17 September 2020 - * @version 0.1 - * @brief Read DMA datas and write them in a file - */ - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include "/home/ubuntu/repositories/KM_comptoncamdmas/files/comptoncamdmas.h" - -typedef struct { - bool debug; - bool toggle; - bool blocking; - bool verbose; - bool redis; - char redis_prefix[256]; - redisContext *redis_context; - - char input_device_name[256]; - int input_device_handler; - - bool output_file_1_enable; - char output_file_1_name[256]; - FILE *output_file_1_handler; - - bool output_file_2_enable; - char output_file_2_name[256]; - FILE *output_file_2_handler; - - uint32_t max_block_size; - uint32_t max_count; - - bool max_file_size_enable; - uint32_t max_file_size; - uint32_t file_name_id; - - uint32_t* data; - - char prefix_filename[256]; -} ConfigDMAToRaw, *pConfigDMAToRaw; - -int configure_s2mm (int file_desc) -{ - int ret_val; - uint32_t reg0a = 0xaa; - uint32_t args_for_configure_s2mm[2]; - args_for_configure_s2mm[0] = (uint32_t) 0; - args_for_configure_s2mm[1] = (uint32_t) ®0a; - - ret_val = ioctl(file_desc, CONFIGURE_S2MM, &args_for_configure_s2mm[0]); - - return ret_val; -} - -int manage_command_line (pConfigDMAToRaw pConfig, int argc, char * const* argv){ - int opt; - - while((opt = getopt(argc, argv, ":i:o:k:c:s:df:tbvr:")) != -1) { - switch(opt) { - case 'i': - printf("input_device_name: %s\n", optarg); - snprintf(pConfig->input_device_name, sizeof pConfig->input_device_name, "%s", optarg); - break; - - case 'o': - printf("output_file_1_name: %s\n", optarg); - snprintf(pConfig->output_file_1_name, sizeof pConfig->output_file_1_name, "%s", optarg); - pConfig->output_file_1_enable = true; - break; - - case 'k': - printf("output_file_2_name: %s\n", optarg); - snprintf(pConfig->output_file_2_name, sizeof pConfig->output_file_2_name, "%s", optarg); - pConfig->output_file_2_enable = true; - break; - - case 'c': - printf("max_count: %s\n", optarg); - pConfig->max_count = atoi (optarg); - break; - - case 's': - printf("max_block_size: %s\n", optarg); - pConfig->max_block_size = atoi (optarg); - break; - - case 'd': - printf("option debug ON\n"); - pConfig->debug = true; - break; - - case 'f': - printf("max_file_size: %s\n", optarg); - pConfig->max_file_size = atoi (optarg); - pConfig->max_file_size_enable = true; - break; - - case 'b': - printf("WARN !!! option blocking read ON\n"); - pConfig->blocking = true; - break; - - case 't': - printf("WARN !!! toggle debug in kernel driver !!!!!!!!!!!\n"); - pConfig->toggle = true; - break; - - case 'v': - printf("verbose mode : ON\n"); - pConfig->verbose = true; - break; - - case 'r': - printf ("REDIS : ON\n"); - snprintf(pConfig->redis_prefix, sizeof pConfig->redis_prefix, "%s", optarg); - pConfig->redis = true; - break; - - case ':': - printf("option needs a value\n"); - break; - - case '?': - printf("unknown option: %c\n", optopt); - break; - } - } - return 0; -} - -int set_a_redis_ex_string (pConfigDMAToRaw pConfig, char * prefix, char * register_name, char * ex, char * value) { - //char tmp_rediscommand[300]; - redisReply *reply; - //snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "SET %s%s %s %s", prefix, register_name, ex, value); - reply = (redisReply *)redisCommand(pConfig->redis_context, "SET %s%s %s %s", prefix, register_name, ex, value); - printf("SET: %s\n", reply->str); - freeReplyObject(reply); - return 0; -} - -int set_a_redis_uint32_t (pConfigDMAToRaw pConfig, char * prefix, char * register_name, char * ex, uint32_t value) { - //char tmp_rediscommand[300]; - redisReply *reply; - //snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "SET %s%s %s %lu", prefix, register_name, ex, (unsigned long) value); - reply = (redisReply *)redisCommand(pConfig->redis_context, "SET %s%s %s %lu", prefix, register_name, ex, (unsigned long) value); - printf("SET: %s\n", reply->str); - freeReplyObject(reply); - return 0; -} - -int get_a_redis_string (pConfigDMAToRaw pConfig, char * prefix, char * register_name, char * result) { - char tmp_rediscommand[300]; - redisReply *reply; - snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "GET %s%s", prefix, register_name); - reply = (redisReply *)redisCommand(pConfig->redis_context, tmp_rediscommand); - printf("*Redis command (%s) -> server returned %s\n", tmp_rediscommand, reply->str); - strcpy(result, reply->str); - freeReplyObject(reply); - return 0; -} - -int get_a_redis_uint32_t (pConfigDMAToRaw pConfig, char * prefix, char * register_name, uint32_t * result) { - char tmp_rediscommand[300]; - redisReply *reply; - snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "GET %s%s", prefix, register_name); - reply = (redisReply *)redisCommand(pConfig->redis_context, tmp_rediscommand); - printf("*Redis command (%s) -> server returned %s\n", tmp_rediscommand, reply->str); - *result = atoi (reply->str); - freeReplyObject(reply); - return 0; -} - -int get_a_redis_bool (pConfigDMAToRaw pConfig, char * prefix, char * register_name, bool * result) { - char tmp_rediscommand[300]; - redisReply *reply; - snprintf (tmp_rediscommand, sizeof tmp_rediscommand, "GET %s%s", prefix, register_name); - reply = (redisReply *)redisCommand(pConfig->redis_context, tmp_rediscommand); - printf("*Redis command (%s) -> server returned %s\n", tmp_rediscommand, reply->str); - *result = atoi (reply->str); - freeReplyObject(reply); - return 0; -} - -int configure_redis_context (pConfigDMAToRaw pConfig) { - setbuf(stdout, NULL); - setbuf(stderr, NULL); - // Open up a connection to Redis - char host[] = "127.0.0.1"; - int port = 6379; - - pConfig->redis_context = redisConnect(host, port); - if (pConfig->redis_context != NULL && pConfig->redis_context->err) { - printf("Error: %sn", pConfig->redis_context->errstr); - return -1; - } - else { - printf("Redis server connected at %s:%d\n", host, port); - } - // Ping Redis server - redisReply *reply; - reply = (redisReply *)redisCommand(pConfig->redis_context,"PING"); - printf("Redis server PING returned %s (%s)\n", reply->str, (!(strcmp(reply->str, "PONG")) ? "SUCCESS" : "FAIL")); - freeReplyObject(reply); - return 0; -} - -int manage_redis (pConfigDMAToRaw pConfig) { - char output_dir_1[256]; - char output_dir_2[256]; - - if (configure_redis_context (pConfig) < 0) {return -1;} - - get_a_redis_bool (pConfig, pConfig->redis_prefix, "_DMA_BLOCKING_MODE", &pConfig->blocking); - get_a_redis_string (pConfig, pConfig->redis_prefix, "_DMA_INPUT", pConfig->input_device_name); - get_a_redis_uint32_t (pConfig, pConfig->redis_prefix, "_DMA_MAX_FILE_SIZE", &pConfig->max_file_size); - if (pConfig->max_file_size > 0) { - pConfig->max_file_size_enable = true; - } - get_a_redis_uint32_t (pConfig, pConfig->redis_prefix, "_DMA_MAX_BLOCK_SIZE", &pConfig->max_block_size); - get_a_redis_string (pConfig, "", "FSW_LOG_PREFIX", pConfig->prefix_filename); - get_a_redis_string (pConfig, "", "DMA_OUTPUT_DIRECTORY_1", output_dir_1); - get_a_redis_string (pConfig, "", "DMA_OUTPUT_DIRECTORY_2", output_dir_2); - get_a_redis_bool (pConfig, "", "DMA_ENABLE_OUTPUT_DIRECTORY_1", &pConfig->output_file_1_enable); - get_a_redis_bool (pConfig, "", "DMA_ENABLE_OUTPUT_DIRECTORY_2", &pConfig->output_file_2_enable); - - snprintf(pConfig->output_file_1_name, sizeof pConfig->output_file_1_name, "%s%s_%s", output_dir_1, pConfig->prefix_filename, pConfig->redis_prefix); - snprintf(pConfig->output_file_2_name, sizeof pConfig->output_file_2_name, "%s%s_%s", output_dir_2, pConfig->prefix_filename, pConfig->redis_prefix); - - return 0; -} - -int open_input_device (pConfigDMAToRaw pConfig) { - pConfig->input_device_handler = open(pConfig->input_device_name, 0); - if (pConfig->input_device_handler < 0) { - printf ("Can't open device file: %s\n", pConfig->input_device_name); - return -1; - } - else { - printf ("Device file opened : %s\n", pConfig->input_device_name); - } - return 0; -} - -int open_new_outputs (pConfigDMAToRaw pConfig) { - char tmp_output_file_name[268]; - - pConfig->file_name_id += 1; - - if (pConfig->output_file_1_enable) { - if (pConfig->output_file_1_handler != NULL) { - fclose (pConfig->output_file_1_handler); - } - snprintf(tmp_output_file_name, sizeof tmp_output_file_name, "%s.%04d", pConfig->output_file_1_name, pConfig->file_name_id); - pConfig->output_file_1_handler = fopen(tmp_output_file_name, "a"); - if (pConfig->output_file_1_handler == NULL) { - printf ("Can't open output file: %s\n", tmp_output_file_name); - pConfig->output_file_1_enable = false; - } - else { - printf ("Output file 1 opened : %s\n", tmp_output_file_name); - } - } - - if (pConfig->output_file_2_enable) { - if (pConfig->output_file_2_handler != NULL) { - fclose (pConfig->output_file_2_handler); - } - snprintf(tmp_output_file_name, sizeof tmp_output_file_name, "%s.%04d", pConfig->output_file_2_name, pConfig->file_name_id); - pConfig->output_file_2_handler = fopen(tmp_output_file_name, "a"); - if (pConfig->output_file_2_handler == NULL) { - printf ("Can't open output file: %s\n", tmp_output_file_name); - pConfig->output_file_2_enable = false; - } - else { - printf ("Output file 2 opened : %s\n", tmp_output_file_name); - } - } - if (!(pConfig->output_file_1_enable | pConfig->output_file_2_enable)) { - return -1; - } - return 0; -} - -int close_all (pConfigDMAToRaw pConfig) { - free (pConfig->data); - if (pConfig->output_file_1_enable) { - fclose (pConfig->output_file_1_handler); - } - if (pConfig->output_file_2_enable) { - fclose (pConfig->output_file_2_handler); - } - close (pConfig->input_device_handler); - - return 0; -} - -int Init_DMA (pConfigDMAToRaw pConfig) { - - uint32_t args_for_ioctl[2]; - uint32_t size_to_write = 0; - pConfig->data = (uint32_t*) calloc ((pConfig->max_block_size >> 2), sizeof (uint32_t)); - args_for_ioctl[0] = (uint32_t) &size_to_write; - args_for_ioctl[1] = (uint32_t) pConfig->data; - - if (pConfig->toggle) - { - ioctl(pConfig->input_device_handler, TOGGLE_DEBUG, &args_for_ioctl[0]); - } - - configure_s2mm (pConfig->input_device_handler); - - return 0; -} - -int Read_DMA_And_Write_File (pConfigDMAToRaw pConfig) { - struct timeval tv1, tv2; - uint32_t size_to_write = 0; - uint32_t size_writen = 0; - uint32_t counter = 0; - uint32_t idx; - - uint32_t args_for_ioctl[2]; - args_for_ioctl[0] = (uint32_t) &size_to_write; - args_for_ioctl[1] = (uint32_t) pConfig->data; - - - do { - gettimeofday (&tv1, NULL); - - size_to_write = pConfig->max_block_size; - if (pConfig->blocking) - { - ioctl(pConfig->input_device_handler, BLOCKING_ALL_IN_ONE_READ, &args_for_ioctl[0]); - } - else - { - ioctl(pConfig->input_device_handler, ALL_IN_ONE_READ, &args_for_ioctl[0]); - } - - if (size_to_write > 0) { - - if (pConfig->max_file_size_enable) { - if (size_writen + size_to_write > pConfig->max_file_size) { - size_writen = 0; - if (open_new_outputs (pConfig) < 0) { - exit (-4); - } - } - } - if (pConfig->output_file_1_enable) { - fwrite (pConfig->data, sizeof (uint32_t), (size_to_write >> 2), pConfig->output_file_1_handler); - } - if (pConfig->output_file_2_enable) { - fwrite (pConfig->data, sizeof (uint32_t), (size_to_write >> 2), pConfig->output_file_2_handler); - } - size_writen += size_to_write; - - if (pConfig->debug) { - idx = 0; - printf ("dma_to_raw_file: all_in_one_read : Data [%03u] = 0x%08x\n", idx, pConfig->data[idx]); - idx = (size_to_write >> 2) - 1; - printf ("dma_to_raw_file: all_in_one_read : Data [%03u] = 0x%08x\n", idx, pConfig->data[idx]); - } - } - - if (pConfig->verbose || size_to_write > 0) { - gettimeofday (&tv2, NULL); - - printf ("%s : all_in_one_read : reg = 0x%x %10d Total time = %f seconds\n", - pConfig->input_device_name, - size_to_write, - counter, - (double) (tv2.tv_usec - tv1.tv_usec) / 1000000 + - (double) (tv2.tv_sec - tv1.tv_sec)); - - } - counter++; - } while ((pConfig->max_count == 0)||(counter < pConfig->max_count)); - - return 0; -} - -int main(int argc, char *argv[]) -{ - ConfigDMAToRaw Config = - {.debug = false, - .toggle = false, - .blocking = false, - .verbose = false, - .redis = false, - .output_file_1_enable = false, - .output_file_2_enable = false, - //.max_block_size = 16384, - .max_block_size = 4095, - .max_count = 0, - .max_file_size_enable = false, - .max_file_size = 0, - .file_name_id = 0, - }; - pConfigDMAToRaw pConfig = &Config; - - manage_command_line (pConfig, argc, argv); - - if (pConfig->redis){ - if (manage_redis (pConfig) < 0) { - exit (-3); - } - } - - if (open_input_device (pConfig) < 0) { - exit(-1); - } - - if (open_new_outputs (pConfig) < 0) { - exit (-2); - } - - Init_DMA (pConfig); - - Read_DMA_And_Write_File (pConfig); - - close_all (pConfig); - - return 0; -} diff --git a/siphractl/MK_WriteConfiguration_251210.py b/siphractl/MK_WriteConfiguration_251210.py deleted file mode 100644 index e973460..0000000 --- a/siphractl/MK_WriteConfiguration_251210.py +++ /dev/null @@ -1,30 +0,0 @@ -import sys -import os -from d2a_lib import D2a -import time -import numpy as np - -if len(sys.argv) < 2: - - print() - print("ERROR! Please pass the binary configuration file as argument!") - print() - sys.exit(1) - -if not os.path.isfile(sys.argv[1]): - - print() - print(f"ERROR! Could not find file \"{sys.argv[1]}\". Please check path!") - print() - sys.exit(1) - -ucd_det = D2a() -ucd_det.sysclk(6) -ucd_det.reset('All') -ucd_det.writeSIPHRAfromFile(sys.argv[1], 'A') -time.sleep(1) -ucd_det.hold1Hz() -print() -print("Configuration",sys.argv[1],"written!") -print() - diff --git a/siphractl/__pycache__/d2a_lib.cpython-314.pyc b/siphractl/__pycache__/d2a_lib.cpython-314.pyc deleted file mode 100644 index ffc7aa05c7a3c568f12b62394e2323848b2005c8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 13671 zcmcgzYiu0Xb)MOsSf&Xi=mnOMXy}9g37h#wH~XY0`EiE{3~9a--$$ z`p&FmGIq?SK|(b_L?v;=CT+vkP9ileKIdNTDJu;Kc*?_nlX&|fLHIAKxDS7W%8KFdr zO}5I>UWWEkvXd{%Ri2Vtg6>BPf>yPzUcIiq zX>I+0l>3%yt;X7PO4%y#S!>t!(&|q6whAMm1ESM=&5HFO@~&a+Jnm}lJXXz!VAbYV zZTyg#Hb3~?0Z^Ep{H%4H0$V_VCik6HuZi1*=B?9524XCcu~aLq#?q;=#CVn&>hoRQ zYWIG1p!Y{pBNoY z8g>v(W^c+e#tnwblw}N@)iW6*?!3uWV$OCQn8T>BQjhwKa7wU5@TzAdXn8f!k~EJc zYhLVJvXy}oy+;^Jv*{W$lZYE<+FSRjskEi)XY@o;$NHCr{>wgFiAGcUq!EqUfoOCx z9nU7I9EwI?&g#kBn{YHbmSCopOr(rd8m~*D(RezBLUlB1TDp~pMRm(!iP5ZOn9(Tn zV|OdGWBc*8iS(q=mNsKLYdgWxpD|*V+2)`wI%t>{u+(NIG80UXS;@Beem$B@jJ9T` zn1bOP5Wku-z!!!8kT$b&xBX)wCcF_b&-Wg85L1y&q}Snu@4TOpI^acTwF;_Mxgd-} zem}KN4|-XAKM&)LUP!7+%ezTPL%9&SAlx9BdML%6lR3sNQzqdGpwdumVr{k{9^xQk1fR}5*gdejPYi#Ety7g zjMbq#Bc?KG=AAotGWuxZjzlV9fzoyBx7F&ts-{sPfEi)As`e}X<;qQ8RUEX`qP4$S zVMIbkU05lBG6IA~)<_@%V3$Xuv7~OA@kGpu8OdZcdReqf3mU;`^eLa@8)KVjAc0E! znY7nme+e7-xm5ds$0OI=6#$$)P^UHMB37c0T=_A;jBparc}^4}HPWw zpE+g5lBW|2y_LB%*s^6zW;&gp!?ri0V+1vIIX~So>^}eGXeq2*MbjG5JX|1}^(350 z5#rfEmK-IYCP7?FG+1y;(R_e@tx<#J#=C%~pdRGbI;3sZ0=%_U3!=453jv0;Qox8- z23W3z0V}i!V5L?LSfy0}R%?}jo3tvx8m$_zR@($vr_}(~YqeGb6h)&}2kLLJz1{ou z=@Wsjbjs2bDVU&SQXR|2QdT038Z%zbCYTW?(*>3=*q9zO)Uyd|LY>fA{46M}!YU+U zhN)T;ES(*nP>&C^2G)p52`~hqP*#2o)}9d>*65a#d2tyMPUNw>TF~GYxz5iaF)C&~ zS#MN0km~>ek=(u~@+G%LfCDw;JwfZQd))DwsCitm91>Fb88k2bbJuMWu(tw-$DPcb zr3v43lJpOMPLLj1_iWAYcu+{?g?&&sK`MXUQ%r%3z=3q#3UU;Sk4)wFNakA=_6WOj zbB}{v8-;|YN!Tnj@qf>L=8$)@kPubjB&k&X_=Gq#Yom~nPLguv&!9Bn(YzVyX(=Hl zgby7CjQQqS1Fu>{_xxgoIw zaRd6?U{=?F?LYEL%*YUDaytqk=01t-8^|QOjWf0vbv@8=jt%zM*{J6)GOS9g3Vke(v~UX!Bh~_V2h= zv-#p{UwiHAug`XWrDqxMdd~Hn?>pBwzjeNIF|=j5ar109wSBekgG!9X62HCg6C!o@ z1rdWQ>KDA%L)(9Q*NfV3@0S&S@f-6Pw*C2sx)kC2Er$=v?^f2L_-;#QN!K>vrw4;w zvj3W(py`?t>}v8}t1RiN_g<@)sk|-N^@R7@-jc2Z-fIVBl-G!H&fN#J5j;!N3H(*tV!<9^d|zEp~uxvgD+31+#;&F zrG8qE$Jr^B%p|m*p-vi;X*Q*Thv9y!U~82)xi!!%IlK%f$vADx894QpEiug4?By(K zOEeSPjZazUIZ>y+J_rS8Pq#Ki9iIgNdn$sz^t^ITIbV9NbS}QAG~7~3XXEE5&P|-3 zJU2Ptd82I0qSCaigw}Lg4p*Lk<=iXhUpx2O{Bt+LP1hC2p01%dsb>6zRv2IE0ocnc zaCFK&L!OVMRGtsqF@jLYCp>cUOurQ19;`Z%K5LvY7*LyT@$qG1!lZMo)g8e>vd_8 zk%lNp@Jzqva5Z5LEX&ehOdYX7Dmys}T&Kr!m@ee+0tWw3{PIv14xnj}WRQ3`&cUZm z9=)DwRRhf;XL#WJd3+$0d1Bf~k3l?Wl&iam2zcd$ynf<;if~Ok#Cye^Sa_X2w*%BK z7bcexp;qhTn9pnl;FPMayV!T3Z(+yZPhOtIAtuZ{T-S^Qo=u!K;Ckt1 z?ju@Mgv4mDvJ5^Mkqi8pHPo>*Q+3q@V+8g~ zQhiuIOiMDD7mI3yLex8!{tLCAWuo}HMpZ%2{w`Bm4AFYw{=L%8WV7Jy%_7m(hx(&Vj!j*n&iF`kmx`&)R&~Izc&Av>&>1z+0)q@fz*|h-4d0CqhMmS7Sjt^u zl9av7x!zXXPqagw{e!*vnmn3KB;)KQPyv|+?lu}IuZn~%^ZnWW{0_K~*dgu>45qoA zGRf^k6kHGp2e^@N#D?pl)%p}Vn_~cwn=)aI+}sXEzR|N7Z2V=ob~)1ceq{SwuU*OwxXqUEkn*uHzd`n^!o?VR>{_C`%Rv=_`i*_~H!Di1rN zmuF(EOJe9H`~DQ)L~$LaiL2sPAVQ*>R|R`|z@FaeqX7_!^Br)nX263JNL#iBo;QuL zY?A18+AuPl?VziW?aL;0=H@@+36rvk@zwx4h|Tn0_Odj7?O@lS_H4AXyIYIyvwgg> z@3O}s5M`T8`W`pW1#d4B2hr$Y? 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Automatically stores a json file with the relevant metadata. -# Written by: Oscar Rosero (KTH) -# .... -# Date: 02/2026 - -import argparse -import subprocess -import json -from pathlib import Path -import time -from datetime import datetime, timezone - -def parse_args(): - parser = argparse.ArgumentParser( - description="Acquire data from SIPHRA ASIC using dma_to_raw_file and store metadata." - ) - - parser.add_argument("-o", "--output", - required=True, - help="Base name for output files (without extension).") - parser.add_argument("-c", "--counts", type=int, default=100_000, - help="Total number of events to acquire.") - parser.add_argument("--active-chs", nargs="*", type=int, default=[], - help="SIPHRA active channels. Receives all active channel numbers (1 -16) separated by a space.") - parser.add_argument("--sipm-chs", nargs="*", default='', - help="Configuration of SIPM channels. One or more strings explaining this configuration.") - parser.add_argument("--source", type=str, default='[NOT SPECIFIED]', - help="A single string containing \'Background\' or any radioactive source present") - parser.add_argument("--source-description", type=str, default='[NOT SPECIFIED]', - help="A single string.") - parser.add_argument("--notes", type=str, default='[NONE]', - help="A single string.") - parser.add_argument("--siphra-config-file", type=str, default='D2a/Ongoing.txt', - help="By default, it takes the \'Ongoing.txt\' file in the \'D2a\' directory and writes its contents to the metadata file. If any other configuration is used, the path to the text file containing it must be specified here.") - parser.add_argument("-s", "--size", type=int, default=4095, - help="[DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS]. Block size. Default is 4095.") - parser.add_argument("--device", default="/dev/D2A_DMA", - help = "[DO NOT CHANGE UNLESS CHANGING dma_to_raw_file SETTINGS].") - - - return parser.parse_args() - -def run_acquisition(args): - output_base = Path(args.output).resolve() - dat_file = output_base.with_suffix(".dat") - - cmd = [ - "./dma_to_raw_file", - "-i", args.device, - "-o", str(dat_file), - "-s", str(args.size), - "-v", - "-c", str(args.counts), - "-b" - ] - - start = time.time() - subprocess.run(cmd, check=True) - end = time.time() - - exposure = end - start - return exposure, output_base - -def write_metadata(output_base, exposure, args): - with open(args.siphra_config_file) as f: - siphra_config = f.readline().strip() - metadata = { - "schema-version": "1.0", - - "acquisition": { - "timestamp_utc": datetime.now(timezone.utc).isoformat(), - "exposure_sec": exposure, - "counts": args.counts, - "active_chs": args.active_chs, - "sipm_chs": args.sipm_chs, - }, - - "source": { - "type": args.source, - "description": args.source_description, - }, - - "SIPHRA_config":{ - "cmd_string": siphra_config, - }, - - "additional_info": { - "data_file": str(output_base.with_suffix(".dat")), - "notes": args.notes, - } - } - - json_file = output_base.with_suffix(".json") - with open(json_file, "w") as f: - json.dump(metadata, f, indent=4) - -def main(): - args = parse_args() - exposure, output_base = run_acquisition(args) - write_metadata(output_base, exposure, args) - print(f"\n\nAcquisition complete.\n\tWritten to: {output_base}\n\tTotal exposure: {exposure:,.2f} seconds.") - -if __name__ == "__main__": - main() - diff --git a/siphractl/d2a_lib.py b/siphractl/d2a_lib.py deleted file mode 100644 index 1dee38e..0000000 --- a/siphractl/d2a_lib.py +++ /dev/null @@ -1,320 +0,0 @@ -'''Script to configure UCD D2 SIPHRA ASICs and reconfigure during flight''' -import mmap -import logging -from construct import BitStruct, Nibble, BitsInteger, ByteSwapped, BitsSwapped -import time -import spidev - -# define Python user-defined exceptions - - -class SPIError(Exception): - 'spidev1.0 not available' - - -class UIOError(Exception): - 'uio0 not available' - - -class SIPHRAWriteError(Exception): - 'SIPHRA write error' - def __init__(self, chip, reg): - self.chip = chip - self.reg = reg - - - - - - -SIPHRA_REG_LENS = [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 14, 24, 23, 6, 8, 18, 6, 19, 15, 6, 2, 2] - -def compareUpTo(a, b, len): - a_zeroed = int.from_bytes(a, byteorder='big') & (2**len - 1) - b_zeroed = int.from_bytes(b, byteorder='big') & (2**len - 1) - return a_zeroed == b_zeroed - - -CHIP2BIN = {'A':1, 'B':2, 'C':4, 'D':8, 'All':15} -ANTICHIP2BIN = {'A':0b1110, 'B':0b1101, 'C':0b1011, 'D':0b0111, 'All':0b0000} -HOLDRATES = {'Off':0, '1Hz':2, '1kHz':1} - -CTRL_ADDR_0 = 4096 -CTRL_0 = BitStruct( - 'hold' / Nibble, - 'reset' / Nibble, - 'cs' / Nibble, - 'sysclk' / Nibble, - ) - -DATA_ADDR_0 = 4104 -DATA_0 = BitsSwapped( BitStruct( - 'error' / Nibble, - 'tempA' / BitsInteger(12), - 'tempB' / BitsInteger(12), - 'pad' / Nibble, - )) - -DATA_ADDR_1 = 0 -DATA_1 = BitsSwapped(BitStruct( - 'tempC' / BitsInteger(12), - 'tempD' / BitsInteger(12) - - )) - - -class D2a: - ''' - Contains all fucntions required to interface with hardware devices through OS. - ''' - def __init__(self, uio='/dev/uio0', spi='/dev/spi'): - try: - self.uio = uio - with open(self.uio, "r+b", 0) as f: - self.reg = mmap.mmap(f.fileno(), length = 4108) - self.deassertCS() - except Exception as exc: - raise UIOError from exc - - try: - self.spi = spidev.SpiDev() - self.spi.open(1,0) - self.spi.mode = 0 - except Exception as exc: - raise SPIError from exc - - def readMMap(self, addr, len=2): - ''' - Read [len] bytes at [addr] from the memory mapped uio file. - ''' - self.reg.seek(addr) - print(self.reg.read(4)) - self.reg.seek(addr) - return self.reg.read(4) - - def writeMMap(self, val, addr): - ''' - Write however many bytes are supplied to the memory mapped - uio file beginning at [addr]. - ''' - self.reg.seek(addr) - self.reg.write(val) - - def readStruct(self, addr, struct): - ''' - Read the appropriate number of bytes from the memmory mapped - uio file beginning at [addr] and parse them through [struct]. - ''' - readBytes = self.readMMap(addr, len = struct.sizeof()) - parsedStruct = struct.parse(readBytes) - return parsedStruct - - def readParam(self, param, addr, struct): - ''' - Like reasdStruct, but returns the value for a single parameter - contained within the struct. - ''' - parsedStruct = self.readStruct(addr, struct) - print(parsedStruct) - return parsedStruct[param] - - def writeParam(self, param, addr, struct, value, clearbits=False): - ''' - Writes a value to a parameter stored in a register. - If the value given is one of the keys of CHIP2BIN (i.e. 'A', 'B', 'C', 'D', 'All'), - then only the bit corresponding to that chip will be set, respecting the existing values - of other chips' bits. Setting clearbits to True clears bits instead of setting them. - ''' - parsedStruct = self.readStruct(addr, struct) - - if value in CHIP2BIN: - if clearbits: - parsedStruct[param] &= ANTICHIP2BIN[value] # clear the bit corresponding to chip. - else: - parsedStruct[param] |= CHIP2BIN[value] # set the bit corresponding to chip. - - else: - parsedStruct[param] = value - - bytesToWrite = struct.build(parsedStruct) - self.writeMMap(bytesToWrite, addr) - - - def keepReset(self, chip): - ''' - Set a reset pin high. - Useful to keep one particular SIPHRA disabled. - ''' - self.writeParam('reset', CTRL_ADDR_0, CTRL_0, chip) - - def releaseReset(self, chip): - ''' - Set a reset pin low. - Used to activate a SIPHRA which had previously been disabled. - ''' - self.writeParam('reset', CTRL_ADDR_0, CTRL_0, chip, clearbits = True) - - def reset(self, chip, toggle_time=0.01): - ''' - Set a reset pin high and then low again after toggle_time. - I.e. actually reset a SIPHRA. - ''' - self.keepReset(chip) - time.sleep(toggle_time) - self.releaseReset(chip) - - - def holdOff(self): - ''' - Disable external triggering. - ''' - self.writeParam('hold', CTRL_ADDR_0, CTRL_0, HOLDRATES['Off']) - - def hold1Hz(self): - ''' - Enable external triggering at a rate of 1Hz. - ''' - self.writeParam('hold', CTRL_ADDR_0, CTRL_0, HOLDRATES['1Hz']) - - def hold1kHz(self): - ''' - Enable external triggering at a rate of 1kHz. - ''' - self.writeParam('hold', CTRL_ADDR_0, CTRL_0, HOLDRATES['1kHz']) - - - def sysclk(self, freq): - ''' - Write a value to the sysclk register to control the sysclk frequency. - - TODO: Make a list of the values and corresponding frequencies. - ''' - self.writeParam('sysclk', CTRL_ADDR_0, CTRL_0, freq) - - - def assertCS(self, chip): - ''' - CS is active low, so we want to set the bit corresponding to chip low - and make sure all the others are high. Probably shouldn't call this with 'All', - but I won't stop you! - ''' - self.writeParam('cs', CTRL_ADDR_0, CTRL_0, ANTICHIP2BIN[chip]) - - def deassertCS(self): - ''' - CS is active low. There isn't really a use case to deassert one at a time. - So we'll just deassert everything by setting them high. - ''' - self.writeParam('cs', CTRL_ADDR_0, CTRL_0, 15) - - - def error(self): - ''' - Returns a single value error 0-15 corresponding to - bitmask of error pins on SIPHRAs A, B, C, D. - ''' - return self.readParam('error', DATA_ADDR_0, DATA_0) - - - def temp(self): - ''' - Returns a dictionary of the four SIPHRA temperatures. - Temperature values are in raw ADC counts. - ''' - tempAB = self.readStruct(DATA_ADDR_0, DATA_0) - tempCD = self.readStruct(DATA_ADDR_1, DATA_1) - temps = { - 'A': tempAB['tempA'], - 'B': tempAB['tempB'], - 'C': tempCD['tempC'], - 'D': tempCD['tempD'], - } - return temps - - - def spiXfer(self, bytelist, chip): - ''' - Version of spi.xfer2 that asserts CS using the D2a GPIOs. - ''' - self.assertCS(chip) - readback = self.spi.xfer2(bytelist, 100_000) - self.deassertCS() - return readback - - def writeSIPHRA(self, val, addr, chip): - ''' - Write a SIPHRA register. - - val: 4-byte bytearray with register contents. - addr: register address. - chip: 'A', 'B', 'C', 'D' or 'All'. - 'All' definitely not recommended. - ''' - write_addr = addr << 1 | 1 - bytes_to_send = [write_addr] + list(val) - self.spiXfer(bytes_to_send, chip) - - def readSIPHRA(self, addr, chip): - ''' - Read a register from SIPHRA. - - addr: register address. - chip: 'A', 'B', 'C', 'D' or 'All'. - 'All' definitely not recommended. - - Returns a four-byte bytearray with register contents. - ''' - read_addr = addr << 1 - bytes_to_send = [read_addr] + [0] * 4 - return bytes(self.spiXfer(bytes_to_send, chip)[1:]) - - def writeSIPHRAwithCheck(self, val, addr, chip): - ''' - Keep writing a register to SIPHRA until - the value you get when reading it back out - matches the expected input. - - TODO: Make the function give up eventually - or try something else like resetting SIPHRA. - ''' - self.writeSIPHRA(val, addr, chip) - remaining_tries = 3 - match = False - while remaining_tries > 0 and match is False: - readback = self.readSIPHRA(addr, chip) - match = compareUpTo(val, readback, SIPHRA_REG_LENS[addr]) - if match is True: - break - else: - remaining_tries -= 1 - if remaining_tries == 0 and match is False: - raise SIPHRAWriteError(chip = chip,reg = addr) - - def writeSIPHRAfromFile(self, filename, chip): - ''' - Write registers to a given SIPHRA chip. - - Assumes every four bytes in a binary file - is a new register value. Will start at - register 0 and keep going while there are - bytes in the file. - - If 'All' is selected for chip, every SIPHRA - gets the same configuration. - ''' - chips = ['A', 'B', 'C', 'D'] if chip == 'All' else [chip] - - vals = [] - with open(filename, 'r+b', 0) as f: - while (val := f.read(4)): - vals.append(val) - - for chip in chips: - for addr, val in enumerate(vals): - print("value:",val) - print("address:",addr) - print("chp:",chip) - self.writeSIPHRAwithCheck(val, addr, chip) - - - diff --git a/siphractl/regs_bit_structure.py b/siphractl/regs_bit_structure.py deleted file mode 100644 index bdfe3a8..0000000 --- a/siphractl/regs_bit_structure.py +++ /dev/null @@ -1,91 +0,0 @@ -from construct import BitStruct, BitsInteger, Padding, Flag - -# ---------- Bit Structures of SIPHRA registers ---------- - -# Registers 0x00 - 0x0F: Channel configuration registers -CHANNEL = BitStruct( - Padding(6), - 'cmis_detector_voffset' / BitsInteger(8), - 'cmis_detector_ioffset' / BitsInteger(3), - 'cmis_impedance_reduction' / BitsInteger(1), - 'cal_select_channel' / Flag, - 'qc_threshold' / BitsInteger(8), - 'qc_hysteresis' / BitsInteger(3), - 'pu_channel' / Flag, - 'enable_triggering' / Flag, -) - -# Register 0x10: Summing channel configuration registers -SUMM_CHANNEL = BitStruct( - Padding(18), - 'cal_select_channel' / Flag, - 'qc_threshold' / BitsInteger(8), - 'qc_hysteresis' / BitsInteger(3), - 'pu_channel' / Flag, - 'enable_triggering' / Flag, -) - -# Register 0x11: Configuration register - channel_config -CHANNEL_CONFIG = BitStruct( - Padding(8), - 'cmis_gain' / BitsInteger(3), - 'ci_gain' / BitsInteger(2), - 'ci_compmode' / BitsInteger(1), - 'shaper_bias' / BitsInteger(4), - 'shaper_feedback_cap' / BitsInteger(4), - 'shaper_feedback_res' / BitsInteger(3), - 'shaper_hold_cap' / BitsInteger(3), - 'shaper_input_cap' / BitsInteger(4), -) - -# Register 0x12: Configuration register - channel_control -CHANNEL_CONTROL = BitStruct( - Padding(9), - 'ci_feedback_dac' / BitsInteger(8), - 'ci_use_reset' / BitsInteger(1), - 'th_select_input' / BitsInteger(1), - 'cb_select_input' / BitsInteger(2), - 'ms_select_input' / BitsInteger(1), - 'cc_enable_dcc' / Flag, - 'cc_threshold' / BitsInteger(8), - 'pt_100_enable_excitation' / Flag, -) - -# Register 0x13: Configuration register - ADC configuration -ADC_CONFIG = BitStruct( - Padding(26), - 'analog_readout_mode' / BitsInteger(1), - 'adc_sample_duration' / BitsInteger(4), - 'adc_mode' / BitsInteger(1), -) - -# Register 0x14: Gain Calibration Unit, Voltage DAC setting -CAL_DAC = BitStruct( - Padding(24), - 'cal_dac' / BitsInteger(8), -) - -# Register 0x15: Power modules -PD_MODULES = BitStruct( - Padding(14), - 'pu_sum_cc' / Flag, - 'pu_sum_ci' / Flag, - 'pu_sum_sha' / Flag, - 'pu_sum_th' / Flag, - 'pu_sum_qc' / Flag, - 'pu_sum_cb' / Flag, - 'pu_cmis' / Flag, - 'pu_cc' / Flag, - 'pu_ci_fb_dac' / Flag, - 'pu_ci' / Flag, - 'pu_sha' / Flag, - 'pu_th' / Flag, - 'pu_qc' / Flag, - 'pu_cb' / Flag, - 'pu_trigger_output_enable' / Flag, - 'pu_adc_ref' / Flag, - 'pu_pt100' / Flag, - 'pu_mb' / Flag, -) - -# \ No newline at end of file diff --git a/siphractl/siphra_controller.py b/siphractl/siphra_controller.py deleted file mode 100644 index 7c2cbbb..0000000 --- a/siphractl/siphra_controller.py +++ /dev/null @@ -1,112 +0,0 @@ -from .d2a_lib import * -from .regs_bit_structure import * - -CH_ADDRS = [] -SIPHRA_RETURN_SIZE = 15 # bytes. Returned by SIPHRA when D2a.readSIPHRA is called -# TODO: Check the size of the packet returned by D2a.readSIPHRA -REG_SIZE = 4 # bytes. Size of the bytearray passed to D2a.writeSIPHRA - -# Register map classes - -class SIPHRARegister: - def __init__(self, addr, structure): - self.addr = addr - self.structure = structure - - def __getitem__(self, param_name): - pass - - def __contains__(self, param_name): - '''True if the register has a parameter named parameter.''' - params = self.structure.subcon.subcons - param_names = [param.name for param in params if param.name] - return param_name in param_names - - def parse(self, value): - return self.structure.parse(value) - - def set_param(self, param_name, value, reg_current_value): - old_register = parse(reg_current_value) - - -class SIPHRARegMap: - def __init__(self): - self._registers = {} - - for i in range(1,17): - self._registers[f"ch{i}"] = SIPHRARegister(i, CHANNEL) - - self._registers["summ"] = SIPHRARegister(0x10, SUMM_CHANNEL) - self._registers["ch_config"] = SIPHRARegister(0x11, CHANNEL_CONFIG) - - def __getitem__(self, name): - return self._registers[name] - - def __getattr__(self, name): - return self._registers[name] - - def __iter__(self): - return iter(self._registers.values()) - - def __len__(self): - return len(self._registers) - - def __contains__(self, name): - return name in self._registers - - def items(self): - return self._registers.items() - - def keys(self): - return self._registers.keys() - - def values(self): - return self._registers.values() - - -class SIPHRA: - def __init__(self, d2a: D2a): - self._d2a = d2a - self._regs = SIPHRARegMap() - - def get_reg_value(self, reg_name, chip='A'): - addr = self._regs[reg_name].addr - reg_value = self._d2a.readSIPHRA(addr, chip)[SIPHRA_RETURN_SIZE - REG_SIZE:] - return reg_value - - def read_register(self, reg_name, chip='A'): - reg_value = self.get_reg_value(reg_name, chip) - strct = self._regs[reg_name].structure - parsed_struct = strct.parse(reg_value) - return parsed_struct - - def _find_reg_containing_param(self, parameter, ch: int=0): - ''' - Returns the address of the register containing the given parameter. - :param parameter: Name of the desired parameter. - :param ch: If the parameter is in one of the 16+1 channel addresses, this argument is used for disambiguation. ``ch`` is a number between 1 and 16 if the parameter belongs to one of the input channels, it is 17 if the parameter belongs to the summing channel and is defaulted to 0 if the parameter belongs to any other register. - :return: The address of the register containing the given parameter. - ''' - reg_addr = None - if not 0 < ch < 17: - raise ValueError(f"Channel {ch} is out of range. Use channels 1-16 and 17 for summing channel") - if ch == 0: # Not a channel register - for reg in self._regs: - if parameter in reg: - reg_addr = reg.addr - else: # Channel register - # Verify that the given register exists - register = self._regs[f"ch{ch}"] - if parameter in register: reg_addr = register.addr - if not reg_addr: - raise NameError(f"Parameter {parameter} does not exist or the register containing it is not implemented.") - return reg_addr - - - - def read_param(self, parameter, ch=0, reg_name=None, chip='A'): - if reg_name: - reg_addr = self._regs[reg_name].addr - else: - reg_addr = self._find_reg_containing_param(parameter, ch) - From 08dbf49f2fef93e06050cd088df2a90b52cae524 Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Fri, 27 Mar 2026 10:36:59 +0100 Subject: [PATCH 08/18] update --- Output.root | Bin 3972 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 Output.root diff --git a/Output.root b/Output.root deleted file mode 100644 index c0b82132702c2a3d18a8cb0c08bbe747f530760a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3972 zcma)5^`w+rNa=z%UFwbc+LufOIz~Al)G)C_{skfV6;g zBQV^NFL&K_KiqrP+ULCQd(QLhv);YG{M=n#Jpq6VZvX&b3jmNuU_u-7EQSdzOi1El zCOZHCdjbF;VFq9&@L7)2r&Atge2WHtCF9-Pd@j*clnPO0N!84oeBUj#|R`bai;_T$d3Qlb+9h~W+hU-({tTro%^fD zHB=K+Auxd3z4UbVasilY5%gmLl!*WctX+L6O&oa&bQ_2evQl6qu1oAvcP4*!G|IauF256$9X-t6Z#5lcR0|IFD1f z(gZF%L?CNbLjt#KcH7u@RIT^)xvT|e>JPs>n~Dr}*4VuJo0p!O?`s{-moL@qZT4<9 zQZ;LI($u+0xmL~$TC$A+SHlDapD51|5$MDTV}96nvrSQz1&8}IR+FCtfO{%=GvlPK z0>I-C!ZHmIVG6lFTc39o6Z|tSdqp{5Ed`O8GbL`>k(!5qeit8jAvOlJO+9mA%w@s` z$yF^}UuDqXw?|C*lCmFKF3gC=Wwt8hv&1z)S#c9hoBZG6;T z^`?6LiYI}eVNKRnCbnjTRV}!WJ(&TSRvE1e@Bk;DC)S}(2Yh}nw`}4BUQqa+m|VST zN~pbBx>z@Ck}a%Ydv01Dd`zC|8@SNI9pnY zyvO>ZADMk$G&JZB@HDqwuHEsWM%U}o&k<(38q(46Fl8CBrF;D#iDlprt z(b|@OyV$ODPjxTB)gVe#+Qm72x7MFLfn&L7Jo`PqtcLvcS{U_&Ylq*4L#laGei}xE%j(Bx%!WUU;x*~Bth<{qs_(^*DGua=RR}h1FOZ0XBY`-=y|B<<@BRJ+3 z$rI#C6jlUgF0|ygIcNn965pQL6I=F#Sb(ZniFG2-2+2p3q$#vJ^p9{xdYHU$Zu#cF zyL`5>up2Q>I%!VNmOzf>aNeF}Uo&4-sYkklvFVn;U%XZr{>^khZm8C z?JYDJ8cRegmI2Fz%9B4LjH&IW7u$lCD?bNS`VIxYFJ^jOa&V(kqxX|GeQ$6)F_K+| zaI&cCjR~Umg)@yl`3MZiYu(9va*M;ZEed>TACJ|E%KsW=z)B#=q2lqG9Q6TP^$W_3 zw{w4@bisUBZvUEHNQ+golW8cNx2bYwpmKV^`zyS`VcM#gEj9Md5mtg(eG%nmXtnj_ z{7NbD$7&iz9$5h}28>N=fD@!1DY@vaT z7J0F>xG!|7;)5OZ%!;ediLvhYUjLBg(omgny>kj7)vVgVC!)o$rS;f4bOOVu2iv${ zKLdZ&N`q4X&*nU&ndcskIYNl5>{slX&h~XDqIAl}faSEA@ z{>C^x2u^(SvAl(&EJ!jdUO(z(8?IgVln2{7Ze9)bV>2UvoNbDqj7IK$OnsV<^gNVX zA8*wrGEtS7v6*!jpZS_VBx6TfI%K-v*4Sk3t@}Y6*XN~fN`?ysnjBdlaEzU*gc(xW z@Do+9n%Uq%hELlBVTpJ?G!3?)#y5k9v{>|~7Nq8{5CHE_@5JLYcqRJo3j^kc( zQUN2=x7iBI`N%%{6_Ix9mqPGg#B^uWXwNO5VI$~BOl+oRBXst(K=FNT)I07=WybV$ zney1@;S zFNZI>u}TFUjIQHw{Qoi zFcWIL>2aQ$%eI_F-R?@ghc?F%V#J1jYP&wX#YBv^5{9BRI}8)w*z~Xfd*f{VA+#M2 zcKRCooY8X_&)S7d){P?euxP!x(v*RKQi>ei?A2;Y|+VFQ8fca!Be>*w4dfrNBE?of6rX%;c$kkV!J(;t$C4xr3_! 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Subject: [PATCH 10/18] Functions for calibration --- analysis/calibration.py | 52 ++ noteboks/baseline_subtraction.ipynb | 664 +++++++++++++++++++ noteboks/energyResolution-Copy1.ipynb | 893 ++++++++++++++++++++++++++ 3 files changed, 1609 insertions(+) create mode 100644 analysis/calibration.py create mode 100644 noteboks/baseline_subtraction.ipynb create mode 100644 noteboks/energyResolution-Copy1.ipynb diff --git a/analysis/calibration.py b/analysis/calibration.py new file mode 100644 index 0000000..3fca9f5 --- /dev/null +++ b/analysis/calibration.py @@ -0,0 +1,52 @@ +import ROOT +import numpy as np + +def calibration_fit(histogram, energy_ranges, energies): + """Function to create a linear calibration fit based on a histogram. Returns slope and constant of linear fit. + Inputs: Histogram to base calibration on. + Ranges within which the peaks of the histogram are located. + Known energies of these peaks, in MeV.""" + + channels = [] + for i in range(len(energy_ranges)): + cal_fit=ROOT.TF1("cal_fit_" + str(i), "gaus", energy_ranges[i][0], energy_ranges[i][1]) + hist.Fit(cal_fit, "R+S") + channels.append(cal_fit.GetParameter('Mean')) + + channels = np.array(channels, dtype='float64') + energies = np.array(energies, dtype='float64') + + graph = ROOT.TGraph(len(channels), channels, energies) + fit = ROOT.TF1("calib", "pol1", min(channels), max(channels)) + graph.Fit(f) + + a = fit.GetParameter(1) + b = fit.GetParameter(0) + + return [a, b] + + +def calibrated_histogram(linear_fit, acquisition, n_of_bins): + a = linear_fit[0] + b = linear_fit[1] + data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b + + emax = a * n_of_bins + b + emin = b + + hist_cal = ROOT.TH1F("h_cal", "Calibrated Spectrum", n_of_bins, emin, emax) + hist_cal.Fill(data_cal) + + return(hist_cal) + + +def calibrated_acquisition(linear_fit, acquisition, n_of_bins): + a = linear_fit[0] + b = linear_fit[1] + print(a, b) + data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b + + emax = a * n_of_bins + b + emin = b + + return(data_cal, emin, emax) \ No newline at end of file diff --git a/noteboks/baseline_subtraction.ipynb b/noteboks/baseline_subtraction.ipynb new file mode 100644 index 0000000..53e5e9f --- /dev/null +++ b/noteboks/baseline_subtraction.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cda6a9c1-c4c0-4e69-b50d-7dcb1b8434f2", + "metadata": {}, + "source": [ + "# Performing baselie subtraction\n", + "\n", + "The baseline-subtracted histograms of an acquisition with 2 active channels were obtained. The baseline value for each channel is the mean of the readings from all the events triggered ONLY by external HOLD assertion. This value is subtracted from the values of each \"real\" event (triggered from internal HOLD).\n", + "\n", + "The dataset for this analysis is a background acquisition of 10 000 events. SIPHRA channels 2 and 14 were connected to SiPM channels 1-4 and 5-8, respectively, using charge comparator, with a threshold value of 20." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:32.276396Z", + "start_time": "2026-02-10T09:37:32.169808Z" + } + }, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "\n", + "from processing import SiphraAcquisition\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# ROOT.gROOT.SetBatch(False)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowEventStatus\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.ShowToolBar\", 1)\n", + "# ROOT.gEnv.SetValue(\"Canvas.UseGL\", 0)\n", + "# ROOT.gROOT.ProcessLine(\"gVirtualX = new TGX11();\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7aa83758-b8f5-4a20-96c3-c8381ea71536", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "# Energy emission peaks in MeV\n", + "K40_MEV = 1.460\n", + "TL208_MEV = 2.614\n", + "CS137_MEV = 0.661\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "68a9cd08dc972f65", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:37:35.457562Z", + "start_time": "2026-02-10T09:37:33.669215Z" + } + }, + "outputs": [], + "source": [ + "# Datasets\n", + "acq_notSubtracted = SiphraAcquisition(project_root+'/data/260203/1_SiPM_ChannelsTest_Ch1-4_Ch2_Ch5-8_Ch14_QT_Thr20_Hys0_Background.csv',\n", + " active_chs=[2,14],\n", + " exposure_sec=1)\n", + "acq_subtracted = SiphraAcquisition(project_root+'/data/260203/1_datdecodertest_subtractBaseline.csv',\n", + " active_chs=[2,14],\n", + " exposure_sec=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cae21a3ee684d69f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T09:43:25.401070Z", + "start_time": "2026-02-10T09:43:24.995723Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "none of the 2 overloaded methods succeeded. Full details:\n void TH1::FillN(Int_t, const Double_t*, const Double_t*, const Double_t*, Int_t) =>\n TypeError: takes at least 5 arguments (3 given)\n could not convert argument to buffer or nullptr", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 24\u001b[39m\n\u001b[32m 22\u001b[39m hist_singlech = ROOT.TH1F(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCh. \u001b[39m\u001b[38;5;132;01m{\u001b[39;00macq.active_chs[\u001b[32m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m, BITS9, \u001b[32m0\u001b[39m, BITS12)\n\u001b[32m 23\u001b[39m hist.Fill(acq[ch]/\u001b[38;5;28mlen\u001b[39m(acq.active_chs))\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m \u001b[43mhist_singlech\u001b[49m\u001b[43m.\u001b[49m\u001b[43mFill\u001b[49m\u001b[43m(\u001b[49m\u001b[43macq\u001b[49m\u001b[43m[\u001b[49m\u001b[43macq\u001b[49m\u001b[43m.\u001b[49m\u001b[43mactive_chs\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 25\u001b[39m hist.Scale(\u001b[32m1\u001b[39m/acq.exposure)\n\u001b[32m 26\u001b[39m hist_singlech.Scale(\u001b[32m1\u001b[39m/acq.exposure)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/COMCUBE/lib/python3.14/site-packages/ROOT/_pythonization/_th1.py:223\u001b[39m, in \u001b[36m_FillWithNumpyArray\u001b[39m\u001b[34m(self, *args)\u001b[39m\n\u001b[32m 219\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(weights) != \u001b[38;5;28mlen\u001b[39m(data):\n\u001b[32m 220\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 221\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLength mismatch: data length (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m) != weights length (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(weights)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 222\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m223\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mFillN\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 224\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 225\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._Fill(*args)\n", + "\u001b[31mTypeError\u001b[39m: none of the 2 overloaded methods succeeded. Full details:\n void TH1::FillN(Int_t, const Double_t*, const Double_t*, const Double_t*, Int_t) =>\n TypeError: takes at least 5 arguments (3 given)\n could not convert argument to buffer or nullptr" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: Ch. 14 BL subt. (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Ch. 2 No correction (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "SUMMED_XR=5000 # Right limit of the summed channel's x-axis\n", + "\n", + "c = ROOT.TCanvas('cv', 'cv', 1200, 800)\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "names=['BL subt.', 'No correction']\n", + "hists = []\n", + "\n", + "lg = ROOT.TLegend(0.48, 0.61, 0.75, 0.83)\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "for idx, (acq, name) in enumerate(zip([acq_subtracted, acq_notSubtracted], names)):\n", + " # print(f\"Current file: {acq.filepath.name}\")\n", + " ch = 'Summed' #acq.active_chs[0]\n", + " hist = ROOT.TH1F(f\"{ch} {name}\", \"\", int(BITS9*SUMMED_XR/BITS12), 0, SUMMED_XR)\n", + " hist_singlech = ROOT.TH1F(f\"Ch. {acq.active_chs[0]} {name}\", \"\", BITS9, 0, BITS12)\n", + " hist.Fill(acq[ch]/len(acq.active_chs))\n", + " hist_singlech.Fill(acq[acq.active_chs[0]])\n", + " hist.Scale(1/acq.exposure)\n", + " hist_singlech.Scale(1/acq.exposure)\n", + "\n", + " hist_singlech2 = ROOT.TH1F(f\"Ch. {acq.active_chs[1]} {name}\", \"\", BITS9, 0, BITS12)\n", + " hist_singlech2.Fill(acq[acq.active_chs[1]])\n", + " hist_singlech2.Scale(1/acq.exposure)\n", + " #Preeting up..\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Counts\")\n", + " hist.SetLineColor(colors[idx])\n", + " hist.SetTitle(\"Baseline subtraction comparison\")\n", + " # hist.GetYaxis().SetRangeUser(0, 1e4)\n", + " lg.SetHeader(\"SIPHRA Channel\")\n", + " lg.AddEntry(hist, f\"{ch} {name}\", 'l')\n", + " hists.append(hist)\n", + " hists[-1].Draw('sames hist')\n", + " # hist_singlech.GetXaxis().SetTitle(\"ADC channel number\")\n", + " # hist_singlech.GetYaxis().SetTitle(r\"Count rate (s^{-1})\")\n", + " hist_singlech.SetLineColor(colors[idx + 2]+2)\n", + " hist_singlech2.SetLineColor(colors[idx + 4]+1)\n", + " # hist_singlech.SetTitle(\"Baseline subtraction comparison\")\n", + " lg.AddEntry(hist_singlech, f\"Ch. {acq.active_chs[0]} {name}\", 'l')\n", + " lg.AddEntry(hist_singlech2, f\"Ch. {acq.active_chs[1]} {name}\", 'l')\n", + " hists.append(hist_singlech)\n", + " hists[-1].Draw('sames hist')\n", + " hists.append(hist_singlech2)\n", + " hists[-1].Draw('sames hist')\n", + "c.SetLogy()\n", + "ROOT.gPad.Update()\n", + "for i, sp in enumerate(hists):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + "lg.Draw()\n", + "c.Draw()\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fe40546-908d-46cd-868c-f4c3e9bd0902", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "if ROOT.gROOT.FindObject('cv_4plots'):\n", + " ROOT.gROOT.FindObject('cv_4plots').Close()\n", + "\n", + "canvas4 = ROOT.TCanvas('cv_4plots', 'cv_4plots', 1600, 1200)\n", + "canvas4.Divide(2,2)\n", + "\n", + "legends = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "canvas4.cd(1)\n", + "hists[0].Draw('hist')\n", + "hists[3].Draw('sames hist')\n", + "legends[0].AddEntry(hists[0], \"Baseline subtracted\", 'l')\n", + "legends[0].AddEntry(hists[3], \"Not corrected\", 'l')\n", + "legends[0].SetHeader(\"\\'Summed\\' channel spectra\")\n", + "legends[0].Draw()\n", + "canvas4.cd(1).SetLogy()\n", + "\n", + "canvas4.cd(2)\n", + "hists[1].SetXTitle(\"ADC channel number\")\n", + "hists[1].SetYTitle(\"Counts\")\n", + "hists[1].Draw('hist')\n", + "hists[4].Draw('sames hist')\n", + "legends[1].AddEntry(hists[1], \"Baseline subtracted\", 'l')\n", + "legends[1].AddEntry(hists[4], \"Not corrected\", 'l')\n", + "legends[1].SetHeader(\"Ch. 2 spectra\")\n", + "legends[1].Draw()\n", + "canvas4.cd(2).SetLogy()\n", + "\n", + "canvas4.cd(3)\n", + "hists[2].SetXTitle(\"ADC channel number\")\n", + "hists[2].SetYTitle(\"Counts\")\n", + "hists[2].Draw('hist')\n", + "hists[5].Draw('sames hist')\n", + "legends[2].AddEntry(hists[2], \"Baseline subtracted\", 'l')\n", + "legends[2].AddEntry(hists[5], \"Not corrected\", 'l')\n", + "legends[2].SetHeader(\"Ch. 14 spectra\")\n", + "legends[2].Draw()\n", + "canvas4.cd(3).SetLogy()\n", + "\n", + "canvas4.cd(4)\n", + "h0_zoom = hists[0].Clone(\"hists_0_zoomed\")\n", + "h0_zoom.GetXaxis().SetRangeUser(0,BITS12)\n", + "h0_zoom.Draw('hist')\n", + "hists[1].Draw('sames hist')\n", + "hists[2].Draw('sames hist')\n", + "legends[3].AddEntry(h0_zoom, \"\\'Summed\\'\", 'l')\n", + "legends[3].AddEntry(hists[1], \"Ch. 2\", 'l')\n", + "legends[3].AddEntry(hists[2], \"Ch. 14\", 'l')\n", + "legends[3].SetHeader(\"Corrected channels\")\n", + "legends[3].Draw()\n", + "canvas4.cd(4).SetLogy()\n", + "\n", + "canvas4.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46bd074b-d5aa-48a4-b027-f966314995cd", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "print(\"ADC channels range:\\n\"\n", + " f\"{\"\":->50}\\n\"\n", + " \"Channel\\t\\tBL-subtracted\\tNo correction\\n\"\n", + " f\"{\"\":->50}\\n\"\n", + " f\"Summed\\t\\t{acq_subtracted['s'].min()/2} - {acq_subtracted['s'].max()/2}\\t{acq_notSubtracted['s'].min()/2} - {acq_notSubtracted['s'].max()/2}\\n\"\n", + " f\"Ch. 2\\t\\t{acq_subtracted[2].min()} - {acq_subtracted[2].max()}\\t{acq_notSubtracted[2].min()} - {acq_notSubtracted[2].max()}\\n\"\n", + " f\"Ch. 14\\t\\t{acq_subtracted[14].min()} - {acq_subtracted[14].max()}\\t{acq_notSubtracted[14].min()} - {acq_notSubtracted[14].max()}\\n\")\n", + "\n", + "print(\"\\n\\n\"\n", + " \"Number of values above single channel range in \\'Summed\\' channel:\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " \"BL-subtracted\\tNo correction\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " f\"{len(acq_subtracted['s'][acq_subtracted['s'][:]/2>BITS12]):^13}\\t{len(acq_notSubtracted['s'][acq_notSubtracted['s'][:]/2>BITS12]):^13}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae86c5f0-762f-4cca-a8bb-b0c00694959e", + "metadata": {}, + "source": [ + "The baseline subtraction is performed directly at the conversion script. No averaging over the number of channels is carried out by the conversion script, but the above spectra and data correspond to values averaged over the number of active channels (2).\n", + "\n", + "The values of the baselines of all the channels are between 98 and 150.\n", + "\n", + "The range of the summed channel does not exceed the maximum single-channel value (4096) after baseline correction.\n", + "\n", + "**Questions:**\n", + "- Is this baseline correction enough for pedestal subtraction?\n", + "- What about the large peak at the lower range of the spectrum? Is it still originating from noise at this stage?" + ] + }, + { + "cell_type": "markdown", + "id": "19798694-0ef8-43fb-a09c-6b87cc8b03f5", + "metadata": {}, + "source": [ + "# Calibration of corrected spectrum with the backgrond $^{40}$K and $^{208}$Tl peaks\n", + "\n", + "The current process of calibration to actual energies is shown here. The two peaks of $^{40}$K and $^{208}$Tl present in the background are used for this purpose. The emission lines of these nuclides are located at 1.460 MeV and 2.614 MeV, respectively. First, both peaks are fitted to a Gaussian with exponentially-decaying background in the baseline-subtracted background histogram. The Gaussian means are taken as the actual locations of the emission lines. The raw data is linearly transformed to aligh with these two points.\n", + "\n", + "To verify the calibration, we use the same calibration parameters to fit a spectrum taken in the pressence of a sample of $^{137}$Cs. After background subtraction, it is expected that the mean of the Gaussian fit of the resulting peak, lands at 0.661MeV." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4f39db9-ad0d-4f5b-b3b5-ea67040593a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Datasets\n", + "folder = Path(project_root)/'data/260209'\n", + "times_lst = [0, 119.634, 161.062, 148.920, 164.828, 34.836, 80.792, 54.386, 90.837, 24.645, 24.144, 24.606, 23.381, 24.720, 24.916, 24.848, 24.934] # Index coincide with starting number of file name\n", + "\n", + "# Select SiPM channels 5-8, SIPHRA channel 14 -> gives cleaner signal\n", + "# Background is file with #4, source is file with #16, with preffix 'BLsubtr_'\n", + "BGIDX = 4; SRCIDX=16; ACTIVECH=14\n", + "bgfile = sorted(folder.glob(f'BLsubtr_{BGIDX}*.csv'))[0]\n", + "srcfile = sorted(folder.glob(f'BLsubtr_{SRCIDX}*.csv'))[0]\n", + "\n", + "acq_bg = SiphraAcquisition(bgfile, active_chs=ACTIVECH, exposure_sec=times_lst[BGIDX])\n", + "acq_src = SiphraAcquisition(srcfile, active_chs=ACTIVECH, exposure_sec=times_lst[SRCIDX])\n", + "colors_clb = [colors[1]-1, colors[3]-1, colors[0]-2] # colors for BG, signal and clean, respectively\n", + "NORMFACTOR = acq_src.exposure/(acq_bg.exposure)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b44ed9c-6f45-4174-8f81-1407bcdacc05", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "print(\"ADC channels range after baseline subtraction:\\n\"\n", + " \"CHANNEL 14\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " \"Data set\\tRange\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " f\"Background\\t{acq_bg[ACTIVECH].min()} - {acq_bg[ACTIVECH].max()}\\n\"\n", + " f\"Source\\t\\t{acq_src[ACTIVECH].min()} - {acq_src[ACTIVECH].max()}\\n\\n\"\n", + " \"SUMMED CHANNEL\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " \"Data set\\tRange\\n\"\n", + " f\"{\"\":->30}\\n\"\n", + " f\"Background\\t{acq_bg['s'].min()} - {acq_bg['s'].max()}\\n\"\n", + " f\"Source\\t\\t{acq_src['s'].min()} - {acq_src['s'].max()}\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "cddfadfd-d1a7-4257-9712-15c6daf6d565", + "metadata": {}, + "source": [ + "Baseline subtraction is effectively filtering out noise from inactive channels. It is noteworthy that, when subtracting single channels, the upper ADC channels will be empty, hence there is a loss of dynamic range due to baseline subtraction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2093b302-1f12-4e19-86f6-74506d4e5ff6", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "# Raw histograms\n", + "hist_bg = ROOT.TH1F(\"Background\", \"\", BITS9, 0, BITS12)\n", + "hist_bg.Fill(acq_bg[ACTIVECH])\n", + "hist_bg.Scale(NORMFACTOR) # Normalize counts to the source exposure time\n", + "\n", + "hist_src = ROOT.TH1F(\"Signal 137Cs\", \"\", BITS9, 0, BITS12)\n", + "hist_src.Fill(acq_src[ACTIVECH])\n", + "\n", + "# Bacground subtraction\n", + "hist_clean = hist_src.Clone(\"137Cs no BG\")\n", + "hist_clean.Add(hist_bg, -1)\n", + "# Removing negative bins\n", + "for i in range(1, hist_clean.GetNbinsX() + 1): # bin 0 is the underflow\n", + " if hist_clean.GetBinContent(i) < 0: hist_clean.SetBinContent(i, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eacf6495-684b-42fa-9eab-8ca73fb2afef", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_bg.GetXaxis().SetTitle(\"ADC Channel\")\n", + "hist_bg.GetYaxis().SetTitle(\"Normalized counts\")\n", + "hist_bg.SetLineColor(colors_clb[0])\n", + "hist_bg.SetFillColor(colors_clb[0])\n", + "hist_src.SetLineColor(colors_clb[1])\n", + "hist_src.SetFillColorAlpha(colors_clb[1], 0.8)\n", + "lg1.AddEntry(hist_bg, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_bg.Draw(\"hist\")\n", + "hist_src.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "hist_bg.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean.GetXaxis().SetTitle(\"ADC Channel\")\n", + "hist_clean.GetYaxis().SetTitle(\"Normalized counts\")\n", + "hist_clean.SetLineColor(colors_clb[2])\n", + "hist_clean.SetFillColor(colors_clb[2])\n", + "hist_clean.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b94d6b2-ea7d-41ea-acb2-a7e820e9dd34", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "# Fit background peaks\n", + "xrange_k40 = (600,1000)\n", + "nrm_k40_i = 55\n", + "mean_k40_i = 740\n", + "xrange_tl208 = (1000,1400)\n", + "nrm_tl208_i = 7\n", + "mean_tl208_i = 1210\n", + "hist_bg_fit = hist_bg.Clone()\n", + "hist_bg_fit.GetYaxis().SetRangeUser(0,1e3)\n", + "hist_bg.SetTitle(r\"^{40}K and ^{208}Tl peaks fitting\")\n", + "fit_fn_k40, fit_res_k40 = fit_peak_expbg(hist_bg_fit, name=\"K40_peak\", xl=xrange_k40[0], xr=xrange_k40[1], norm=nrm_k40_i, mean=mean_k40_i, showFit=True)\n", + "fit_fn_tl208, fit_res_tl208 = fit_peak_expbg(hist_bg_fit, name=\"Tl208_peak\", xl=xrange_tl208[0], xr=xrange_tl208[1], norm=nrm_tl208_i, mean=mean_tl208_i, showFit=True)\n", + "\n", + "cv_fit = ROOT.TCanvas(\"cv_fit\", \"cv_fit\", 800,600)\n", + "hist_bg_fit.SetFillStyle(0)\n", + "hist_bg_fit.Draw(\"hist\")\n", + "cv_fit.SetLogy()\n", + "cv_fit.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e0906bf-c280-4c5e-b555-871735d01a54", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibrate data and get calibrated histograms\n", + "K40FIT = fit_fn_k40.GetParameter('Mean')\n", + "TL208FIT = fit_fn_tl208.GetParameter('Mean')\n", + "clb_slope = (TL208_MEV - K40_MEV)/(TL208FIT - K40FIT)\n", + "clb_const = K40_MEV - clb_slope * K40FIT\n", + "\n", + "data_bg_clb = acq_bg[ACTIVECH]*clb_slope + clb_const\n", + "data_src_clb = acq_src[ACTIVECH]*clb_slope + clb_const\n", + "\n", + "CLBHISTS_XR = np.max([data_bg_clb.max(), data_src_clb.max()])\n", + "\n", + "hist_bg_clb = ROOT.TH1F(\"Calibrated background\", \"Calibrated Histograms\", BITS9, 0, CLBHISTS_XR)\n", + "hist_src_clb = ROOT.TH1F(\"Calibrated signal\", \"Calibrated Histograms\", BITS9, 0, CLBHISTS_XR)\n", + "\n", + "hist_bg_clb.Fill(data_bg_clb)\n", + "hist_bg_clb.Scale(NORMFACTOR)\n", + "\n", + "hist_src_clb.Fill(data_src_clb)\n", + "\n", + "hist_clean_clb = hist_src_clb.Clone(\"Calibrated signal no BG\")\n", + "hist_clean_clb.Add(hist_bg_clb, -1)\n", + "\n", + "for hist, clr in zip([hist_bg_clb, hist_src_clb, hist_clean_clb], colors_clb):\n", + " hist.GetXaxis().SetTitle(\"Energy (MeV)\")\n", + " hist.GetYaxis().SetTitle(\"Normalized counts\")\n", + " hist.SetLineColor(clr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5416087a-578e-4d42-8157-2516db3c6b39", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_bg_clb.SetFillColor(colors_clb[0])\n", + "hist_src_clb.SetFillColorAlpha(colors_clb[1], 0.8)\n", + "lg1.AddEntry(hist_bg_clb, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_bg_clb.Draw(\"hist\")\n", + "hist_src_clb.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean_clb.SetFillColor(colors_clb[2])\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcbad448-c208-4054-a4ab-e2b71b7ed2a4", + "metadata": {}, + "outputs": [], + "source": [ + "res = hist_clean_clb.Fit(\"gaus\", \"L S\", \"\", 0, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d7f6090-75e8-4779-8a1c-35f1cad0a43d", + "metadata": {}, + "outputs": [], + "source": [ + "cv_fit = ROOT.TCanvas(\"cv_fit\", \"cv_fit\", 800,600)\n", + "# hist_bg_fit.SetFillStyle(0)\n", + "hist_clean_clb.Draw(\"hist\")\n", + "hist_clean_clb.SetFillColor(colors_clb[2]-6)\n", + "cv_fit.SetLogy()\n", + "cv_fit.Draw()\n", + "\n", + "\n", + "err = (CS137_MEV - hist_clean_clb.GetFunction(\"gaus\").GetParameter(\"Mean\"))/CS137_MEV\n", + "print(f\"Error in the location of Cs peak: {err*100:.1f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58f9c3b4-03c0-453a-9d37-cd9cd565ad5f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "sigma = 0.136089\n", + "FWHM = np.sqrt(8*np.log(2))*sigma\n", + "print(f\"{FWHM=:.2f} Energy resolution at 480MeV: {FWHM*100/0.479418:.2f}%\")" + ] + } + ], + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/noteboks/energyResolution-Copy1.ipynb b/noteboks/energyResolution-Copy1.ipynb new file mode 100644 index 0000000..d68f13a --- /dev/null +++ b/noteboks/energyResolution-Copy1.ipynb @@ -0,0 +1,893 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4a3b1bc5-ec73-4d69-a840-07d057f4e2eb", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition, MetadataLoader\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "\n", + "# Energy emission peaks in MeV\n", + "Cs137_MeV = 0.662\n", + "Na22_MeV = [0.511, 1.275, 1.786]\n", + "Co60_MeV = [1.173, 1.332, 2,505]\n", + "Am241_MeV = 0.0595\n", + "# Background emission\n", + "K40_MeV = 1.460\n", + "Tl208_MEV = 2.614\n", + "\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75493f51-2089-4f0a-8057-0f2c2bf7466f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[SIPHRAAcquisition(File: 'SUBT_01_EnergyResolution_thr30_gain1over100_1over3_Background'), SIPHRAAcquisition(File: 'SUBT_02_EnergyResolution_thr30_gain1over100_1over3_Cs137'), SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'), SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22'), SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'), SIPHRAAcquisition(File: 'SUBT_06_EnergyResolution_thr30_gain1over100_1over3_Co60'), SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'), SIPHRAAcquisition(File: 'SUBT_08_EnergyResolution_thr30_gain1over100_1over3_Am241'), SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]\n" + ] + } + ], + "source": [ + "files = sorted((project_root/'data/260323').glob('SUBT_*'))\n", + "acqs = [SiphraAcquisition(file) for file in files]\n", + "print(acqs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "88f4c856-78c0-4ca6-ad58-cc10f3b53649", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cs-137\n", + "Na22\n", + "Co-60\n", + "Am-241\n" + ] + } + ], + "source": [ + "# histograms\n", + "hists = {}\n", + "sources = []\n", + "for sgnl, bg in zip(acqs[1::2], acqs[2::2]):\n", + " filepath = sgnl.filepath.stem\n", + " src = (MetadataLoader.load(sgnl.metadataFile)).source\n", + " sources.append(src)\n", + " print(src)\n", + " # Signal and Background\n", + " hist_sgnl = ROOT.TH1F(f\"{src} Signal\", \"\", BITS12, 0, BITS12)\n", + " hist_bg = ROOT.TH1F(f\"{src} Background\", \"\", BITS12, 0 , BITS12)\n", + " hist_sgnl.Fill(sgnl['s']/len(sgnl.active_chs))\n", + " hist_bg.Fill(bg['s']/len(bg.active_chs))\n", + " hist_bg.Scale(sgnl.exposure/bg.exposure)\n", + " # Clean spectrum\n", + " hist_clean = hist_sgnl.Clone(f\"{src} Clean\")\n", + " hist_clean.Add(hist_bg, -1)\n", + " for hist in [hist_sgnl, hist_bg, hist_clean]:\n", + " # hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Normalized counts\")\n", + " hists[src] = hist_sgnl\n", + " hists[f\"{src}_BG\"] = hist_bg\n", + " hists[f\"{src}_clean\"] = hist_clean\n", + " del hist_sgnl, hist_bg\n", + "#print(hists)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "36f654a6-bd64-471f-a0e3-01927db52e3e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", + "cv.Divide(2,2)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", + " cv.cd(idx+1)\n", + " \n", + " sgnl = hists[src]\n", + " bg = hists[src+'_BG']\n", + " clean = hists[src+'_clean']\n", + " \n", + " lg.AddEntry(sgnl, \"Signal\")\n", + " lg.AddEntry(bg, \"Background\")\n", + " lg.AddEntry(clean, \"Subtracted\")\n", + " sgnl.SetLineColor(colors[0])\n", + " bg.SetLineColor(colors[1])\n", + " clean.SetLineColor(colors[2])\n", + " sgnl.SetTitle(src)\n", + " sgnl.Draw(\"hist\")\n", + " bg.Draw(\"sames hist\")\n", + " clean.Draw(\"sames hist\")\n", + " ROOT.gPad.Update()\n", + " for i, sp in enumerate([sgnl, bg, clean]):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " lg.Draw()\n", + " cv.cd(idx+1).SetLogy()\n", + " cv.Draw()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6db7ba78-ddd9-4d76-8bba-bf310f8f1221", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.004280158214429103\n", + "-0.2655932438428593\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 196.518\n", + "NDf = 127\n", + "Edm = 2.29967e-07\n", + "NCalls = 65\n", + "Constant = 2459.66 +/- 7.74946 \n", + "Mean = 183.993 +/- 0.078652 \n", + "Sigma = 29.2438 +/- 0.0713573 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 356.977\n", + "NDf = 147\n", + "Edm = 1.20533e-06\n", + "NCalls = 64\n", + "Constant = 1361.97 +/- 6.38965 \n", + "Mean = 275.54 +/- 0.144272 \n", + "Sigma = 32.7382 +/- 0.141925 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 52.0578\n", + "NDf = 47\n", + "Edm = 1.53695e-06\n", + "NCalls = 107\n", + "Constant = 255.901 +/- 3.65957 \n", + "Mean = 478.856 +/- 0.861944 \n", + "Sigma = 32.2107 +/- 1.8725 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 50.431\n", + "NDf = 47\n", + "Edm = 6.26293e-06\n", + "NCalls = 110\n", + "Constant = 187.956 +/- 4.04943 \n", + "Mean = 177.384 +/- 1.67095 \n", + "Sigma = 37.5292 +/- 4.35406 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 152.171\n", + "NDf = 97\n", + "Edm = 2.80563e-08\n", + "NCalls = 96\n", + "Constant = 606.675 +/- 4.80363 \n", + "Mean = 371.93 +/- 0.452134 \n", + "Sigma = 45.3973 +/- 0.782852 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 63.6754\n", + "NDf = 67\n", + "Edm = 1.95162e-06\n", + "NCalls = 89\n", + "Constant = 880.588 +/- 5.38234 \n", + "Mean = 471.391 +/- 0.435139 \n", + "Sigma = 32.0034 +/- 0.470993 \t (limited)\n", + "****************************************\n", + "Minimizer is Linear / Migrad\n", + "Chi2 = 0.00408946\n", + "NDf = 1\n", + "p0 = -0.265593 +/- 0.109303 \n", + "p1 = 0.00428016 +/- 0.000302376 \n" + ] + } + ], + "source": [ + "#Fitting all the visible peaks. Values around peak input manually\n", + "\n", + "Cs137_fit = ROOT.TF1(\"Cs137_fit\", \"gaus\", 120,250)\n", + "\n", + "Co60a_fit = ROOT.TF1(\"Co60a_fit\", \"gaus\", 200,350)\n", + "Co60b_fit = ROOT.TF1(\"Co60b_fit\", \"gaus\", 450,500)\n", + "\n", + "Na22a_fit = ROOT.TF1(\"Na22a_fit\", \"gaus\", 150,200)\n", + "Na22b_fit = ROOT.TF1(\"Na22b_fit\", \"gaus\", 320,420)\n", + "Na22c_fit = ROOT.TF1(\"Na22c_fit\", \"gaus\", 450,520)\n", + "\n", + "fits = [Cs137_fit, Co60a_fit, Co60b_fit, Na22a_fit, Na22b_fit, Na22c_fit]\n", + "\n", + "#Fit to histogram and find mean value of peak\n", + "hists['Cs-137_clean'].Fit(Cs137_fit, \"R+S\")\n", + "hists['Co-60_clean'].Fit(Co60a_fit, \"R+S\")\n", + "hists['Co-60_clean'].Fit(Co60b_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22a_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22b_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22c_fit, \"R+S\")\n", + "\n", + "Cs137_mean = Cs137_fit.GetParameter('Mean')\n", + "Co60a_mean = Co60a_fit.GetParameter('Mean')\n", + "Co60b_mean = Co60b_fit.GetParameter('Mean')\n", + "Na22a_mean = Na22a_fit.GetParameter('Mean')\n", + "Na22b_mean = Na22b_fit.GetParameter('Mean')\n", + "Na22c_mean = Na22c_fit.GetParameter('Mean')\n", + "\n", + "channels = [Na22a_mean, Na22b_mean, Na22c_mean]\n", + "energies = Na22_MeV\n", + "channels = np.array(channels, dtype='float64')\n", + "energies = np.array(energies, dtype='float64')\n", + "\n", + "g = ROOT.TGraph(len(channels), channels, energies)\n", + "\n", + "f = ROOT.TF1(\"calib\", \"pol1\", min(channels), max(channels))\n", + "g.Fit(f)\n", + "\n", + "a = f.GetParameter(1)\n", + "b = f.GetParameter(0)\n", + "print(a)\n", + "print(b)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "\n", + "#lg.Draw()\n", + "#cv.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6b5a5060-fe8f-48ba-af47-c12bca5ea672", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.265593239861784\n", + "17.265934723735494\n", + "17.265934723735494\n", + "-0.265593239861784\n", + "SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22')\n", + "SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4')\n", + "[0.96495224 0.7338237 0.70760773 ... 0.76057469 0.40532156 0.49787998]\n", + "16.08942624009443\n", + "-0.11364762396634862\n", + "4096\n", + "4096\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Calibrated background (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Calibrated signal (Potential memory leak).\n" + ] + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv_cal'):\n", + " ROOT.gROOT.FindObject('cv_cal').Close()\n", + "\n", + "\n", + " \n", + "hist = hists['Na22_clean']\n", + "\n", + "emin = a * hist.GetXaxis().GetXmin() + b\n", + "emax = a * hist.GetXaxis().GetXmax() + b\n", + "\n", + "emax_2 = a * BITS12 + b\n", + "emin_2 = a * 0 + b\n", + "\n", + "print(emin)\n", + "print(emin_2)\n", + "print(emax)\n", + "print(emax_2)\n", + "\n", + "print(acqs[3])\n", + "print(acqs[4])\n", + "\n", + "data_bg_clb = a * (acqs[4]['s']/len(acqs[4].active_chs)) + b\n", + "data_src_clb = a * (acqs[3]['s']/len(acqs[4].active_chs)) + b\n", + "\n", + "print(data_bg_clb)\n", + "\n", + "print(data_bg_clb.max())\n", + "print(data_bg_clb.min())\n", + "\n", + "h_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\",\n", + " hist.GetNbinsX(), emin, emax)\n", + "\n", + "print(hist.GetNbinsX())\n", + "print(BITS12)\n", + "\n", + "#for i in range(1, hist.GetNbinsX() + 1):\n", + "# counts = hist.GetBinContent(i)\n", + "# h_cal.SetBinContent(i, counts)\n", + "\n", + "hist_bg_clb = ROOT.TH1F(\"Calibrated background\", \"Calibrated Histograms\", BITS12, emin, emax)\n", + "hist_src_clb = ROOT.TH1F(\"Calibrated signal\", \"Calibrated Histograms\", BITS12, emin, emax)\n", + "\n", + "\n", + "hist_bg_clb.Fill(data_bg_clb)\n", + "#hist_bg_clb.Scale(NORMFACTOR)\n", + "\n", + "hist_src_clb.Fill(data_src_clb)\n", + "\n", + "hist_clean_clb = hist_src_clb.Clone(\"Calibrated signal no BG\")\n", + "hist_clean_clb.Add(hist_bg_clb, -1)\n", + "\n", + "#for hist, clr in zip([hist_bg_clb, hist_src_clb, hist_clean_clb], colors):\n", + "# hist.GetXaxis().SetTitle(\"Energy (MeV)\")\n", + " # hist.GetYaxis().SetTitle(\"Normalized counts\")\n", + " # hist.SetLineColor(clr) \n", + "#\n", + "#cv_cal.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "57b1d3c2-9d5b-43c3-8b49-fd081e3fc77f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in : Cannot set Y axis to log scale\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_bg_clb.SetLineColor(colors[0])\n", + "hist_src_clb.SetLineColor(colors[1])\n", + "lg1.AddEntry(hist_bg_clb, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_bg_clb.Draw(\"hist\")\n", + "hist_src_clb.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean_clb.SetLineColor(colors[2])\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "79a219b6-cf1a-419e-825f-272accfa28eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 137.425\n", + "NDf = 67\n", + "Edm = 2.14133e-06\n", + "NCalls = 84\n", + "Constant = 190.012 +/- 2.40871 \n", + "Mean = 0.492756 +/- 0.00341755 \n", + "Sigma = 0.142388 +/- 0.00438193 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 465.001\n", + "NDf = 91\n", + "Edm = 1.22703e-07\n", + "NCalls = 75\n", + "Constant = 611.78 +/- 3.97344 \n", + "Mean = 1.31966 +/- 0.00146494 \n", + "Sigma = 0.178741 +/- 0.0023569 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 742.699\n", + "NDf = 90\n", + "Edm = 7.47939e-06\n", + "NCalls = 95\n", + "Constant = 827.73 +/- 4.69558 \n", + "Mean = 1.74335 +/- 0.00131288 \n", + "Sigma = 0.172477 +/- 0.00195917 \t (limited)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in : Cannot set Y axis to log scale\n" + ] + } + ], + "source": [ + "# Plot all the histograms\n", + "\n", + "\n", + "\n", + "\n", + "Na22a_fit_cal = ROOT.TF1(\"Na22a_fit_cal\", \"gaus\", 0.3, 0.6)\n", + "Na22b_fit_cal = ROOT.TF1(\"Na22b_fit_cal\", \"gaus\", 1.1 , 1.5)\n", + "Na22c_fit_cal = ROOT.TF1(\"Na22c_fit_cal\", \"gaus\", 1.5, 1.9)\n", + "hist_clean_clb.Fit(Na22a_fit_cal, \"R+S\")\n", + "hist_clean_clb.Fit(Na22b_fit_cal, \"R+S\")\n", + "hist_clean_clb.Fit(Na22c_fit_cal, \"R+S\")\n", + "\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(idx+1).SetLogy()\n", + "\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "8947e352-94db-4b13-8666-c1f2d7eca282", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.0, 0.0]\n", + "0.0 0.0\n", + "0.0 0.0\n", + "SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4')\n", + "(array([0., 0., 0., ..., 0., 0., 0.], shape=(46706,)), 0.0, 0.0)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 50.431\n", + "NDf = 47\n", + "Edm = 6.26293e-06\n", + "NCalls = 110\n", + "Constant = 187.956 +/- 4.04943 \n", + "Mean = 177.384 +/- 1.67095 \n", + "Sigma = 37.5292 +/- 4.35406 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 152.171\n", + "NDf = 97\n", + "Edm = 2.80563e-08\n", + "NCalls = 96\n", + "Constant = 606.675 +/- 4.80363 \n", + "Mean = 371.93 +/- 0.452134 \n", + "Sigma = 45.3973 +/- 0.782852 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 63.6754\n", + "NDf = 67\n", + "Edm = 1.95162e-06\n", + "NCalls = 89\n", + "Constant = 880.588 +/- 5.38234 \n", + "Mean = 471.391 +/- 0.435139 \n", + "Sigma = 32.0034 +/- 0.470993 \t (limited)\n", + "****************************************\n", + "Minimizer is Linear / Migrad\n", + "Chi2 = 0.00408946\n", + "NDf = 1\n", + "p0 = -0.265593 +/- 0.109303 \n", + "p1 = 0.00428016 +/- 0.000302376 \n" + ] + } + ], + "source": [ + "def calibration_fit(histogram, energy_ranges, energies):\n", + " \"\"\"Function to create a linear calibration fit based on a histogram. Returns slope and constant of linear fit. \n", + " Inputs: Histogram to base calibration on.\n", + " Ranges within which the peaks of the histogram are located.\n", + " Known energies of these peaks, in MeV.\"\"\"\n", + " \n", + " channels = []\n", + " for i in range(len(energy_ranges)):\n", + " cal_fit=ROOT.TF1(\"cal_fit_\" + str(i), \"gaus\", energy_ranges[i][0], energy_ranges[i][1])\n", + " hist.Fit(cal_fit, \"R+S\")\n", + " channels.append(cal_fit.GetParameter('Mean'))\n", + "\n", + " channels = np.array(channels, dtype='float64')\n", + " energies = np.array(energies, dtype='float64')\n", + "\n", + " graph = ROOT.TGraph(len(channels), channels, energies)\n", + " fit = ROOT.TF1(\"calib\", \"pol1\", min(channels), max(channels))\n", + " graph.Fit(f)\n", + " \n", + " a = fit.GetParameter(1)\n", + " b = fit.GetParameter(0)\n", + "\n", + " return [a, b]\n", + "\n", + "def calibrated_histogram(linear_fit, acquisition, n_of_bins):\n", + " a = linear_fit[0]\n", + " b = linear_fit[1]\n", + " data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b\n", + "\n", + " emax = a * n_of_bins + b\n", + " emin = b\n", + "\n", + " hist_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\", n_of_bins, emin, emax)\n", + " hist_cal.Fill(data_cal)\n", + "\n", + " return(hist_cal)\n", + "\n", + "def calibrated_acquisition(linear_fit, acquisition):\n", + " a = linear_fit[0]\n", + " b = linear_fit[1]\n", + " print(a, b)\n", + " data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b\n", + "\n", + " emax = a * BITS12 + b\n", + " emin = b\n", + "\n", + " return(data_cal, emin, emax)\n", + "\n", + " \n", + " \n", + "hist = hists['Na22_clean']\n", + "energy_ranges = [(150, 200), (320, 420), (450, 520)]\n", + "energies = Na22_MeV\n", + "\n", + "c_fit = calibration_fit(hist, energy_ranges,energies)\n", + "hist_cal_bg = calibrated_histogram(c_fit, acqs[4])\n", + "hist_cal_Na22 = calibrated_histogram(c_fit, acqs[3])\n", + "hist_cal_clean = hist_cal_Na22.Clone(\"Calibrated signal no BG\")\n", + "\n", + "#hist_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\", BITS12, emin, emax)\n", + "#hist_cal.Fill(data_cal)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a9b090a7-1c19-4ea1-8676-176a371a3f62", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "calibration_hist.SetLineColor(colors[0])\n", + "hist_src_clb.SetLineColor(colors[1])\n", + "lg1.AddEntry(calibration_hist, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", + "calibration_hist.Draw(\"hist\")\n", + "hist_src_clb.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "#hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean_clb.SetLineColor(colors[2])\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f60a5136-480e-48d5-b6e2-8b32ff255bbb", + "metadata": {}, + "outputs": [], + "source": [ + "def energy_Resolution(histogram, peak_range" + ] + } + ], + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 997cc20ca0673111fbea3ea4de3151d22a2e8d88 Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Mon, 30 Mar 2026 15:46:55 +0200 Subject: [PATCH 11/18] Added new functions --- analysis/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/analysis/__init__.py b/analysis/__init__.py index b5e352a..f516f04 100644 --- a/analysis/__init__.py +++ b/analysis/__init__.py @@ -1,3 +1,4 @@ from .fit import * +from .calibration import * -__all__ = ['gauspeak', 'bg_exp', 'peak_and_bg', 'fit_peak_expbg'] \ No newline at end of file +__all__ = ['gauspeak', 'bg_exp', 'peak_and_bg', 'fit_peak_expbg', 'calibration_fit', 'calibrated_histogram', 'calibrated_acquisition' ] \ No newline at end of file From bfb17372cb28ef56b1f9907459eef376d64ba210 Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Mon, 30 Mar 2026 15:58:51 +0200 Subject: [PATCH 12/18] new file --- notebooks/energyResolution-Copy2.ipynb | 893 +++++++++++++++++++++++++ 1 file changed, 893 insertions(+) create mode 100644 notebooks/energyResolution-Copy2.ipynb diff --git a/notebooks/energyResolution-Copy2.ipynb b/notebooks/energyResolution-Copy2.ipynb new file mode 100644 index 0000000..d68f13a --- /dev/null +++ b/notebooks/energyResolution-Copy2.ipynb @@ -0,0 +1,893 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4a3b1bc5-ec73-4d69-a840-07d057f4e2eb", + "metadata": {}, + "outputs": [], + "source": [ + "import ROOT\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "import ipynbname\n", + "from pathlib import Path\n", + "\n", + "project_root = str(ipynbname.path().parent.parent)\n", + "sys.path.append(project_root)\n", + "project_root=Path(project_root)\n", + "\n", + "from processing import SiphraAcquisition, MetadataLoader\n", + "from analysis import fit_peak_expbg\n", + "\n", + "# Constants\n", + "BITS12 = 2**12\n", + "BITS9 = 2**9 # 512 typical number of bins used\n", + "\n", + "# Energy emission peaks in MeV\n", + "Cs137_MeV = 0.662\n", + "Na22_MeV = [0.511, 1.275, 1.786]\n", + "Co60_MeV = [1.173, 1.332, 2,505]\n", + "Am241_MeV = 0.0595\n", + "# Background emission\n", + "K40_MeV = 1.460\n", + "Tl208_MEV = 2.614\n", + "\n", + "colors = [ROOT.kRed, ROOT.kBlue, ROOT.kGreen, ROOT.kOrange, ROOT.kViolet, ROOT.kYellow, ROOT.kSpring, ROOT.kCyan,]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75493f51-2089-4f0a-8057-0f2c2bf7466f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[SIPHRAAcquisition(File: 'SUBT_01_EnergyResolution_thr30_gain1over100_1over3_Background'), SIPHRAAcquisition(File: 'SUBT_02_EnergyResolution_thr30_gain1over100_1over3_Cs137'), SIPHRAAcquisition(File: 'SUBT_03_EnergyResolution_thr30_gain1over100_1over3_Background2'), SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22'), SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4'), SIPHRAAcquisition(File: 'SUBT_06_EnergyResolution_thr30_gain1over100_1over3_Co60'), SIPHRAAcquisition(File: 'SUBT_07_EnergyResolution_thr30_gain1over100_1over3_Background6'), SIPHRAAcquisition(File: 'SUBT_08_EnergyResolution_thr30_gain1over100_1over3_Am241'), SIPHRAAcquisition(File: 'SUBT_09_EnergyResolution_thr30_gain1over100_1over3_Background8')]\n" + ] + } + ], + "source": [ + "files = sorted((project_root/'data/260323').glob('SUBT_*'))\n", + "acqs = [SiphraAcquisition(file) for file in files]\n", + "print(acqs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "88f4c856-78c0-4ca6-ad58-cc10f3b53649", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cs-137\n", + "Na22\n", + "Co-60\n", + "Am-241\n" + ] + } + ], + "source": [ + "# histograms\n", + "hists = {}\n", + "sources = []\n", + "for sgnl, bg in zip(acqs[1::2], acqs[2::2]):\n", + " filepath = sgnl.filepath.stem\n", + " src = (MetadataLoader.load(sgnl.metadataFile)).source\n", + " sources.append(src)\n", + " print(src)\n", + " # Signal and Background\n", + " hist_sgnl = ROOT.TH1F(f\"{src} Signal\", \"\", BITS12, 0, BITS12)\n", + " hist_bg = ROOT.TH1F(f\"{src} Background\", \"\", BITS12, 0 , BITS12)\n", + " hist_sgnl.Fill(sgnl['s']/len(sgnl.active_chs))\n", + " hist_bg.Fill(bg['s']/len(bg.active_chs))\n", + " hist_bg.Scale(sgnl.exposure/bg.exposure)\n", + " # Clean spectrum\n", + " hist_clean = hist_sgnl.Clone(f\"{src} Clean\")\n", + " hist_clean.Add(hist_bg, -1)\n", + " for hist in [hist_sgnl, hist_bg, hist_clean]:\n", + " # hist.SetTitle(r\"^{22}Na spectra - CI gain = 1/0.75 pC\")\n", + " hist.GetXaxis().SetTitle(\"ADC channel number\")\n", + " hist.GetYaxis().SetTitle(r\"Normalized counts\")\n", + " hists[src] = hist_sgnl\n", + " hists[f\"{src}_BG\"] = hist_bg\n", + " hists[f\"{src}_clean\"] = hist_clean\n", + " del hist_sgnl, hist_bg\n", + "#print(hists)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "36f654a6-bd64-471f-a0e3-01927db52e3e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", + "cv.Divide(2,2)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", + " cv.cd(idx+1)\n", + " \n", + " sgnl = hists[src]\n", + " bg = hists[src+'_BG']\n", + " clean = hists[src+'_clean']\n", + " \n", + " lg.AddEntry(sgnl, \"Signal\")\n", + " lg.AddEntry(bg, \"Background\")\n", + " lg.AddEntry(clean, \"Subtracted\")\n", + " sgnl.SetLineColor(colors[0])\n", + " bg.SetLineColor(colors[1])\n", + " clean.SetLineColor(colors[2])\n", + " sgnl.SetTitle(src)\n", + " sgnl.Draw(\"hist\")\n", + " bg.Draw(\"sames hist\")\n", + " clean.Draw(\"sames hist\")\n", + " ROOT.gPad.Update()\n", + " for i, sp in enumerate([sgnl, bg, clean]):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " lg.Draw()\n", + " cv.cd(idx+1).SetLogy()\n", + " cv.Draw()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6db7ba78-ddd9-4d76-8bba-bf310f8f1221", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.004280158214429103\n", + "-0.2655932438428593\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 196.518\n", + "NDf = 127\n", + "Edm = 2.29967e-07\n", + "NCalls = 65\n", + "Constant = 2459.66 +/- 7.74946 \n", + "Mean = 183.993 +/- 0.078652 \n", + "Sigma = 29.2438 +/- 0.0713573 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 356.977\n", + "NDf = 147\n", + "Edm = 1.20533e-06\n", + "NCalls = 64\n", + "Constant = 1361.97 +/- 6.38965 \n", + "Mean = 275.54 +/- 0.144272 \n", + "Sigma = 32.7382 +/- 0.141925 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 52.0578\n", + "NDf = 47\n", + "Edm = 1.53695e-06\n", + "NCalls = 107\n", + "Constant = 255.901 +/- 3.65957 \n", + "Mean = 478.856 +/- 0.861944 \n", + "Sigma = 32.2107 +/- 1.8725 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 50.431\n", + "NDf = 47\n", + "Edm = 6.26293e-06\n", + "NCalls = 110\n", + "Constant = 187.956 +/- 4.04943 \n", + "Mean = 177.384 +/- 1.67095 \n", + "Sigma = 37.5292 +/- 4.35406 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 152.171\n", + "NDf = 97\n", + "Edm = 2.80563e-08\n", + "NCalls = 96\n", + "Constant = 606.675 +/- 4.80363 \n", + "Mean = 371.93 +/- 0.452134 \n", + "Sigma = 45.3973 +/- 0.782852 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 63.6754\n", + "NDf = 67\n", + "Edm = 1.95162e-06\n", + "NCalls = 89\n", + "Constant = 880.588 +/- 5.38234 \n", + "Mean = 471.391 +/- 0.435139 \n", + "Sigma = 32.0034 +/- 0.470993 \t (limited)\n", + "****************************************\n", + "Minimizer is Linear / Migrad\n", + "Chi2 = 0.00408946\n", + "NDf = 1\n", + "p0 = -0.265593 +/- 0.109303 \n", + "p1 = 0.00428016 +/- 0.000302376 \n" + ] + } + ], + "source": [ + "#Fitting all the visible peaks. Values around peak input manually\n", + "\n", + "Cs137_fit = ROOT.TF1(\"Cs137_fit\", \"gaus\", 120,250)\n", + "\n", + "Co60a_fit = ROOT.TF1(\"Co60a_fit\", \"gaus\", 200,350)\n", + "Co60b_fit = ROOT.TF1(\"Co60b_fit\", \"gaus\", 450,500)\n", + "\n", + "Na22a_fit = ROOT.TF1(\"Na22a_fit\", \"gaus\", 150,200)\n", + "Na22b_fit = ROOT.TF1(\"Na22b_fit\", \"gaus\", 320,420)\n", + "Na22c_fit = ROOT.TF1(\"Na22c_fit\", \"gaus\", 450,520)\n", + "\n", + "fits = [Cs137_fit, Co60a_fit, Co60b_fit, Na22a_fit, Na22b_fit, Na22c_fit]\n", + "\n", + "#Fit to histogram and find mean value of peak\n", + "hists['Cs-137_clean'].Fit(Cs137_fit, \"R+S\")\n", + "hists['Co-60_clean'].Fit(Co60a_fit, \"R+S\")\n", + "hists['Co-60_clean'].Fit(Co60b_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22a_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22b_fit, \"R+S\")\n", + "hists['Na22_clean'].Fit(Na22c_fit, \"R+S\")\n", + "\n", + "Cs137_mean = Cs137_fit.GetParameter('Mean')\n", + "Co60a_mean = Co60a_fit.GetParameter('Mean')\n", + "Co60b_mean = Co60b_fit.GetParameter('Mean')\n", + "Na22a_mean = Na22a_fit.GetParameter('Mean')\n", + "Na22b_mean = Na22b_fit.GetParameter('Mean')\n", + "Na22c_mean = Na22c_fit.GetParameter('Mean')\n", + "\n", + "channels = [Na22a_mean, Na22b_mean, Na22c_mean]\n", + "energies = Na22_MeV\n", + "channels = np.array(channels, dtype='float64')\n", + "energies = np.array(energies, dtype='float64')\n", + "\n", + "g = ROOT.TGraph(len(channels), channels, energies)\n", + "\n", + "f = ROOT.TF1(\"calib\", \"pol1\", min(channels), max(channels))\n", + "g.Fit(f)\n", + "\n", + "a = f.GetParameter(1)\n", + "b = f.GetParameter(0)\n", + "print(a)\n", + "print(b)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "\n", + "#lg.Draw()\n", + "#cv.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6b5a5060-fe8f-48ba-af47-c12bca5ea672", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.265593239861784\n", + "17.265934723735494\n", + "17.265934723735494\n", + "-0.265593239861784\n", + "SIPHRAAcquisition(File: 'SUBT_04_EnergyResolution_thr30_gain1over100_1over3_Na22')\n", + "SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4')\n", + "[0.96495224 0.7338237 0.70760773 ... 0.76057469 0.40532156 0.49787998]\n", + "16.08942624009443\n", + "-0.11364762396634862\n", + "4096\n", + "4096\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Calibrated background (Potential memory leak).\n", + "Warning in : Replacing existing TH1: Calibrated signal (Potential memory leak).\n" + ] + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv_cal'):\n", + " ROOT.gROOT.FindObject('cv_cal').Close()\n", + "\n", + "\n", + " \n", + "hist = hists['Na22_clean']\n", + "\n", + "emin = a * hist.GetXaxis().GetXmin() + b\n", + "emax = a * hist.GetXaxis().GetXmax() + b\n", + "\n", + "emax_2 = a * BITS12 + b\n", + "emin_2 = a * 0 + b\n", + "\n", + "print(emin)\n", + "print(emin_2)\n", + "print(emax)\n", + "print(emax_2)\n", + "\n", + "print(acqs[3])\n", + "print(acqs[4])\n", + "\n", + "data_bg_clb = a * (acqs[4]['s']/len(acqs[4].active_chs)) + b\n", + "data_src_clb = a * (acqs[3]['s']/len(acqs[4].active_chs)) + b\n", + "\n", + "print(data_bg_clb)\n", + "\n", + "print(data_bg_clb.max())\n", + "print(data_bg_clb.min())\n", + "\n", + "h_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\",\n", + " hist.GetNbinsX(), emin, emax)\n", + "\n", + "print(hist.GetNbinsX())\n", + "print(BITS12)\n", + "\n", + "#for i in range(1, hist.GetNbinsX() + 1):\n", + "# counts = hist.GetBinContent(i)\n", + "# h_cal.SetBinContent(i, counts)\n", + "\n", + "hist_bg_clb = ROOT.TH1F(\"Calibrated background\", \"Calibrated Histograms\", BITS12, emin, emax)\n", + "hist_src_clb = ROOT.TH1F(\"Calibrated signal\", \"Calibrated Histograms\", BITS12, emin, emax)\n", + "\n", + "\n", + "hist_bg_clb.Fill(data_bg_clb)\n", + "#hist_bg_clb.Scale(NORMFACTOR)\n", + "\n", + "hist_src_clb.Fill(data_src_clb)\n", + "\n", + "hist_clean_clb = hist_src_clb.Clone(\"Calibrated signal no BG\")\n", + "hist_clean_clb.Add(hist_bg_clb, -1)\n", + "\n", + "#for hist, clr in zip([hist_bg_clb, hist_src_clb, hist_clean_clb], colors):\n", + "# hist.GetXaxis().SetTitle(\"Energy (MeV)\")\n", + " # hist.GetYaxis().SetTitle(\"Normalized counts\")\n", + " # hist.SetLineColor(clr) \n", + "#\n", + "#cv_cal.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "57b1d3c2-9d5b-43c3-8b49-fd081e3fc77f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in : Cannot set Y axis to log scale\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_bg_clb.SetLineColor(colors[0])\n", + "hist_src_clb.SetLineColor(colors[1])\n", + "lg1.AddEntry(hist_bg_clb, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_bg_clb.Draw(\"hist\")\n", + "hist_src_clb.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "# lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean_clb.SetLineColor(colors[2])\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "79a219b6-cf1a-419e-825f-272accfa28eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 137.425\n", + "NDf = 67\n", + "Edm = 2.14133e-06\n", + "NCalls = 84\n", + "Constant = 190.012 +/- 2.40871 \n", + "Mean = 0.492756 +/- 0.00341755 \n", + "Sigma = 0.142388 +/- 0.00438193 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 465.001\n", + "NDf = 91\n", + "Edm = 1.22703e-07\n", + "NCalls = 75\n", + "Constant = 611.78 +/- 3.97344 \n", + "Mean = 1.31966 +/- 0.00146494 \n", + "Sigma = 0.178741 +/- 0.0023569 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 742.699\n", + "NDf = 90\n", + "Edm = 7.47939e-06\n", + "NCalls = 95\n", + "Constant = 827.73 +/- 4.69558 \n", + "Mean = 1.74335 +/- 0.00131288 \n", + "Sigma = 0.172477 +/- 0.00195917 \t (limited)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error in : Cannot set Y axis to log scale\n" + ] + } + ], + "source": [ + "# Plot all the histograms\n", + "\n", + "\n", + "\n", + "\n", + "Na22a_fit_cal = ROOT.TF1(\"Na22a_fit_cal\", \"gaus\", 0.3, 0.6)\n", + "Na22b_fit_cal = ROOT.TF1(\"Na22b_fit_cal\", \"gaus\", 1.1 , 1.5)\n", + "Na22c_fit_cal = ROOT.TF1(\"Na22c_fit_cal\", \"gaus\", 1.5, 1.9)\n", + "hist_clean_clb.Fit(Na22a_fit_cal, \"R+S\")\n", + "hist_clean_clb.Fit(Na22b_fit_cal, \"R+S\")\n", + "hist_clean_clb.Fit(Na22c_fit_cal, \"R+S\")\n", + "\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(idx+1).SetLogy()\n", + "\n", + "ROOT.gPad.Modified()\n", + "ROOT.gPad.Update()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "8947e352-94db-4b13-8666-c1f2d7eca282", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.0, 0.0]\n", + "0.0 0.0\n", + "0.0 0.0\n", + "SIPHRAAcquisition(File: 'SUBT_05_EnergyResolution_thr30_gain1over100_1over3_Background4')\n", + "(array([0., 0., 0., ..., 0., 0., 0.], shape=(46706,)), 0.0, 0.0)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 50.431\n", + "NDf = 47\n", + "Edm = 6.26293e-06\n", + "NCalls = 110\n", + "Constant = 187.956 +/- 4.04943 \n", + "Mean = 177.384 +/- 1.67095 \n", + "Sigma = 37.5292 +/- 4.35406 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 152.171\n", + "NDf = 97\n", + "Edm = 2.80563e-08\n", + "NCalls = 96\n", + "Constant = 606.675 +/- 4.80363 \n", + "Mean = 371.93 +/- 0.452134 \n", + "Sigma = 45.3973 +/- 0.782852 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 63.6754\n", + "NDf = 67\n", + "Edm = 1.95162e-06\n", + "NCalls = 89\n", + "Constant = 880.588 +/- 5.38234 \n", + "Mean = 471.391 +/- 0.435139 \n", + "Sigma = 32.0034 +/- 0.470993 \t (limited)\n", + "****************************************\n", + "Minimizer is Linear / Migrad\n", + "Chi2 = 0.00408946\n", + "NDf = 1\n", + "p0 = -0.265593 +/- 0.109303 \n", + "p1 = 0.00428016 +/- 0.000302376 \n" + ] + } + ], + "source": [ + "def calibration_fit(histogram, energy_ranges, energies):\n", + " \"\"\"Function to create a linear calibration fit based on a histogram. Returns slope and constant of linear fit. \n", + " Inputs: Histogram to base calibration on.\n", + " Ranges within which the peaks of the histogram are located.\n", + " Known energies of these peaks, in MeV.\"\"\"\n", + " \n", + " channels = []\n", + " for i in range(len(energy_ranges)):\n", + " cal_fit=ROOT.TF1(\"cal_fit_\" + str(i), \"gaus\", energy_ranges[i][0], energy_ranges[i][1])\n", + " hist.Fit(cal_fit, \"R+S\")\n", + " channels.append(cal_fit.GetParameter('Mean'))\n", + "\n", + " channels = np.array(channels, dtype='float64')\n", + " energies = np.array(energies, dtype='float64')\n", + "\n", + " graph = ROOT.TGraph(len(channels), channels, energies)\n", + " fit = ROOT.TF1(\"calib\", \"pol1\", min(channels), max(channels))\n", + " graph.Fit(f)\n", + " \n", + " a = fit.GetParameter(1)\n", + " b = fit.GetParameter(0)\n", + "\n", + " return [a, b]\n", + "\n", + "def calibrated_histogram(linear_fit, acquisition, n_of_bins):\n", + " a = linear_fit[0]\n", + " b = linear_fit[1]\n", + " data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b\n", + "\n", + " emax = a * n_of_bins + b\n", + " emin = b\n", + "\n", + " hist_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\", n_of_bins, emin, emax)\n", + " hist_cal.Fill(data_cal)\n", + "\n", + " return(hist_cal)\n", + "\n", + "def calibrated_acquisition(linear_fit, acquisition):\n", + " a = linear_fit[0]\n", + " b = linear_fit[1]\n", + " print(a, b)\n", + " data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b\n", + "\n", + " emax = a * BITS12 + b\n", + " emin = b\n", + "\n", + " return(data_cal, emin, emax)\n", + "\n", + " \n", + " \n", + "hist = hists['Na22_clean']\n", + "energy_ranges = [(150, 200), (320, 420), (450, 520)]\n", + "energies = Na22_MeV\n", + "\n", + "c_fit = calibration_fit(hist, energy_ranges,energies)\n", + "hist_cal_bg = calibrated_histogram(c_fit, acqs[4])\n", + "hist_cal_Na22 = calibrated_histogram(c_fit, acqs[3])\n", + "hist_cal_clean = hist_cal_Na22.Clone(\"Calibrated signal no BG\")\n", + "\n", + "#hist_cal = ROOT.TH1F(\"h_cal\", \"Calibrated Spectrum\", BITS12, emin, emax)\n", + "#hist_cal.Fill(data_cal)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a9b090a7-1c19-4ea1-8676-176a371a3f62", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all the histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "calibration_hist.SetLineColor(colors[0])\n", + "hist_src_clb.SetLineColor(colors[1])\n", + "lg1.AddEntry(calibration_hist, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_src_clb, r\"Signal ^{137}Cs\", \"l\")\n", + "calibration_hist.Draw(\"hist\")\n", + "hist_src_clb.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "#hist_bg_clb.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_clean_clb.SetLineColor(colors[2])\n", + "hist_clean_clb.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f60a5136-480e-48d5-b6e2-8b32ff255bbb", + "metadata": {}, + "outputs": [], + "source": [ + "def energy_Resolution(histogram, peak_range" + ] + } + ], + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 329a01078966796ecae4f0b252e153868c27abb1 Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Tue, 31 Mar 2026 10:55:05 +0200 Subject: [PATCH 13/18] continue working on SIPHRA ctrl --- notebooks/energyResolution-Copy1.ipynb | 2 +- siphractrl/regs_bit_structure.py | 37 +++++++++++++++++++++++--- siphractrl/siphra_controller.py | 34 ++++++++++++++++------- 3 files changed, 58 insertions(+), 15 deletions(-) diff --git a/notebooks/energyResolution-Copy1.ipynb b/notebooks/energyResolution-Copy1.ipynb index f91fc22..9eddcb7 100644 --- a/notebooks/energyResolution-Copy1.ipynb +++ b/notebooks/energyResolution-Copy1.ipynb @@ -531,7 +531,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.3" + "version": "3.14.2" } }, "nbformat": 4, diff --git a/siphractrl/regs_bit_structure.py b/siphractrl/regs_bit_structure.py index 83777ea..2af9b8a 100644 --- a/siphractrl/regs_bit_structure.py +++ b/siphractrl/regs_bit_structure.py @@ -16,7 +16,7 @@ ) # Register 0x10: Summing channel configuration registers -SUMM_CHANNEL = BitStruct( +SUM_CHANNEL = BitStruct( Padding(18), 'cal_select_channel' / Flag, 'qc_threshold' / BitsInteger(8), @@ -89,7 +89,7 @@ ) # Register 0x16 -CARL_CTRL = BitStruct( +CAL_CTRL = BitStruct( Padding(26), 'cal_cv_range_low' / Flag, 'cal_cv_range_mid' / Flag, @@ -99,6 +99,35 @@ ) # Register 0x17 +ch_names = [f'CH_{n}_rofl' for n in range(16, 0, -1)] READOUT_FIXED_LIST = BitStruct( - Padding(8), -) \ No newline at end of file + Padding(13), + 'ADC_IN_rofl' / Flag, + 'SUM_CH_rofl' / Flag, + *[name / Flag for name in ch_names], + 'ADC_PT100_SENSE_rofl' / Flag, +) + +# Register 0x18 +READOUT_MODE = BitStruct( + Padding(17), + 'int_hold_tune' / BitsInteger(5), + 'int_hold_delay' / BitsInteger(4), + 'int_hold_source' / BitsInteger(2), + 'readout_list_mode' / BitsInteger(1), + 'readout_en_spi_forced_start' / Flag, + 'readout_en_ext_hold_start' / Flag, + 'readout_en_int_hold_start' / Flag, +) + +reg_str_lst = [CHANNEL, + SUM_CHANNEL, + CHANNEL_CONFIG, + CHANNEL_CONTROL, + ADC_CONFIG, + CAL_DAC, + PD_MODULES, + CAL_CTRL, + READOUT_FIXED_LIST, + READOUT_MODE, + ] \ No newline at end of file diff --git a/siphractrl/siphra_controller.py b/siphractrl/siphra_controller.py index bcd19cb..6819763 100644 --- a/siphractrl/siphra_controller.py +++ b/siphractrl/siphra_controller.py @@ -1,5 +1,6 @@ from .d2a_lib import * from .regs_bit_structure import * +from collections import namedtuple CH_ADDRS = [] SIPHRA_RETURN_SIZE = 15 # bytes. Returned by SIPHRA when D2a.readSIPHRA is called @@ -7,26 +8,39 @@ REG_SIZE = 4 # bytes. Size of the bytearray passed to D2a.writeSIPHRA # Register map classes +RegField = namedtuple('RegField', ['name', 'size']) class SIPHRARegister: def __init__(self, addr, structure): self.addr = addr self.structure = structure + self.fields = self.structure.subcon.subcons # Includes padding + self.field_names = [field.name for field in self.fields if field.name] + self.size = sum([field.sizeof() for field in self.fields if field.name]) # Number of significant bits in the register + + def __getitem__(self, idx): + '''Returns the name and bit-size of a given field. Index 0 is the padding.''' + if idx >= (n_fields:=len(self.fields)) or idx < 0: + raise IndexError(f"Index {idx} is out of range for register with {n_fields - 1} fields + padding") + if idx == 0: + return RegField(name='Padding', size=self.fields[0].sizeof()) + return RegField(name=self.fields[idx].name, size=self.fields[idx].sizeof()) - def __getitem__(self, param_name): - pass + def __len__(self): + '''Number of fields in this register (excluding padding).''' + return len(self.fields) - 1 - def __contains__(self, param_name): - '''True if the register has a parameter named parameter.''' - params = self.structure.subcon.subcons - param_names = [param.name for param in params if param.name] - return param_name in param_names + def __contains__(self, field_name): + '''True if the register has a field named ``field_name``.''' + return field_name in self.field_names def parse(self, value): return self.structure.parse(value) - def set_param(self, param_name, value, reg_current_value): - old_register = parse(reg_current_value) + def set_param(self, param_name, value, current_content): + reg_content = parse(current_content) + reg_content[param_name] = value + return self.structure.build(reg_content) class SIPHRARegMap: @@ -36,7 +50,7 @@ def __init__(self): for i in range(1,17): self._registers[f"ch{i}"] = SIPHRARegister(i, CHANNEL) - self._registers["summ"] = SIPHRARegister(0x10, SUMM_CHANNEL) + self._registers["chsum"] = SIPHRARegister(0x10, SUM_CHANNEL) self._registers["ch_config"] = SIPHRARegister(0x11, CHANNEL_CONFIG) def __getitem__(self, name): From c71701f21e742ec1d913e7458709d773f3cee8fd Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Tue, 31 Mar 2026 10:58:48 +0200 Subject: [PATCH 14/18] new function --- notebooks/energyResolution-Copy2.ipynb | 196 +++++++++++++++---------- 1 file changed, 116 insertions(+), 80 deletions(-) diff --git a/notebooks/energyResolution-Copy2.ipynb b/notebooks/energyResolution-Copy2.ipynb index d68f13a..9e22d77 100644 --- a/notebooks/energyResolution-Copy2.ipynb +++ b/notebooks/energyResolution-Copy2.ipynb @@ -117,21 +117,21 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "
\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if ROOT.gROOT.FindObject('cv'):\n", + " ROOT.gROOT.FindObject('cv').Close()\n", + "\n", + "Yinit = 0.82 # For stat boxes\n", + "\n", + "cv = ROOT.TCanvas('cv', 'cv', 1600, 1200)\n", + "cv.Divide(2,2)\n", + "\n", + "ROOT.gStyle.SetOptStat(11)\n", + "ROOT.gStyle.SetStatFontSize(0.03)\n", + "ROOT.gStyle.SetStatW(0.16)\n", + "\n", + "lgds = [ROOT.TLegend(0.48, 0.61, 0.75, 0.83) for _ in range(4)]\n", + "\n", + "for idx, (src, lg) in enumerate(zip(sources, lgds)): \n", + " cv.cd(idx+1)\n", + " \n", + " sgnl = hists[src]\n", + " bg = hists[src+'_BG']\n", + " clean = hists[src+'_clean']\n", + " \n", + " lg.AddEntry(sgnl, \"Signal\")\n", + " lg.AddEntry(bg, \"Background\")\n", + " lg.AddEntry(clean, \"Subtracted\")\n", + " sgnl.SetLineColor(colors[0])\n", + " bg.SetLineColor(colors[1])\n", + " clean.SetLineColor(colors[2])\n", + " sgnl.SetTitle(src)\n", + " sgnl.Draw(\"hist\")\n", + " bg.Draw(\"sames hist\")\n", + " clean.Draw(\"sames hist\")\n", + " ROOT.gPad.Update()\n", + " for i, sp in enumerate([sgnl, bg, clean]):\n", + " st = sp.FindObject(\"stats\")\n", + " # print(type(st))\n", + " st.SetY1NDC(Yinit - i*0.08)\n", + " st.SetY2NDC(0.1)\n", + " lg.Draw()\n", + " cv.cd(idx+1).SetLogy()\n", + " cv.Draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9ecbda3c-0946-404a-afff-9e8014ef5a2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.004280158214429103, -0.2655932438428593]\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 50.431\n", + "NDf = 47\n", + "Edm = 6.26293e-06\n", + "NCalls = 110\n", + "Constant = 187.956 +/- 4.04943 \n", + "Mean = 177.384 +/- 1.67095 \n", + "Sigma = 37.5292 +/- 4.35406 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 152.171\n", + "NDf = 97\n", + "Edm = 2.80563e-08\n", + "NCalls = 96\n", + "Constant = 606.675 +/- 4.80363 \n", + "Mean = 371.93 +/- 0.452134 \n", + "Sigma = 45.3973 +/- 0.782852 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 63.6754\n", + "NDf = 67\n", + "Edm = 1.95162e-06\n", + "NCalls = 89\n", + "Constant = 880.588 +/- 5.38234 \n", + "Mean = 471.391 +/- 0.435139 \n", + "Sigma = 32.0034 +/- 0.470993 \t (limited)\n", + "****************************************\n", + "Minimizer is Linear / Migrad\n", + "Chi2 = 0.00408946\n", + "NDf = 1\n", + "p0 = -0.265593 +/- 0.109303 \n", + "p1 = 0.00428016 +/- 0.000302376 \n" + ] + } + ], + "source": [ + "from analysis import *\n", + "\n", + "#Calibration based on the 3 peaks of the Na-22 spectrum\n", + "\n", + "\n", + "hist = hists['Na22_clean']\n", + "energy_ranges = [(150, 200), (320, 420), (450, 520)]\n", + "energies = Na22_MeV\n", + "\n", + "c_fit = calibration_fit(hist, energy_ranges, energies)\n", + "print(c_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "22724987-e04a-4617-8c06-9fa511199544", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.4476679045176995]\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 297.546\n", + "NDf = 170\n", + "Edm = 2.83736e-09\n", + "NCalls = 66\n", + "Constant = 25.7351 +/- 0.0778773 \n", + "Mean = 0.52178 +/- 0.00031843 \n", + "Sigma = 0.125841 +/- 0.000252392 \t (limited)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n", + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Calibrating Cs137 histograms based on Na22 calibration fit\n", + "hist_cal_bg_Cs137 = calibrated_histogram(c_fit, acqs[2], BITS12)\n", + "hist_cal_bg_Cs137.Scale(1/acqs[2].exposure) \n", + "hist_cal_src_Cs137 = calibrated_histogram(c_fit, acqs[1], BITS12)\n", + "hist_cal_src_Cs137.Scale(1/acqs[1].exposure) \n", + "hist_cal_clean_Cs137 = hist_cal_src_Cs137.Clone(\"Calibrated signal no BG\")\n", + "hist_cal_clean_Cs137.Add(hist_cal_bg_Cs137, -1)\n", + "\n", + "\n", + "# Plot the Cs-137 histograms\n", + "if ROOT.gROOT.FindObject('cv1'):\n", + " ROOT.gROOT.FindObject('cv1').Close()\n", + "cv1 = ROOT.TCanvas(\"cv1\", \"cv1\", 1600, 800)\n", + "cv1.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(1)\n", + "hist_cal_bg_Cs137.SetLineColor(colors[0])\n", + "hist_cal_src_Cs137.SetLineColor(colors[1])\n", + "lg1.AddEntry(hist_cal_bg_Cs137, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_cal_src_Cs137, r\"Signal ^{137}Cs\", \"l\")\n", + "hist_cal_src_Cs137.Draw(\"hist\")\n", + "hist_cal_bg_Cs137.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv1.cd(1).SetLogy()\n", + "#hist_cal_bg.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv1.cd(2)\n", + "hist_cal_clean_Cs137.SetLineColor(colors[2])\n", + "hist_cal_clean_Cs137.Draw(\"hist\")\n", + "cv1.cd(2).SetLogy()\n", + "\n", + "cv1.Draw()\n", + "\n", + "#Calculate energy resolution\n", + "res_Cs137 = energy_resolution(hist_cal_clean_Cs137, [(0.11, 0.85)], [Cs137_MeV])\n", + "print(res_Cs137)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "83d50dbc-584b-4327-8dca-120cc181bb0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.6098052497160661, 0.3310662458402524, 0.19985352745576823]\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 114.793\n", + "NDf = 107\n", + "Edm = 1.05832e-07\n", + "NCalls = 78\n", + "Constant = 0.657598 +/- 0.0098944 \n", + "Mean = 0.487964 +/- 0.00391232 \n", + "Sigma = 0.132319 +/- 0.00277695 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 288.638\n", + "NDf = 91\n", + "Edm = 1.06692e-07\n", + "NCalls = 90\n", + "Constant = 2.06376 +/- 0.0165861 \n", + "Mean = 1.31918 +/- 0.00187749 \n", + "Sigma = 0.17924 +/- 0.0030605 \t (limited)\n", + "****************************************\n", + "Minimizer is Minuit2 / Migrad\n", + "Chi2 = 748.173\n", + "NDf = 137\n", + "Edm = 5.20097e-06\n", + "NCalls = 67\n", + "Constant = 2.9031 +/- 0.0151085 \n", + "Mean = 1.73526 +/- 0.00089493 \n", + "Sigma = 0.151566 +/- 0.000800909 \t (limited)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n", + "Warning in : Replacing existing TH1: h_cal (Potential memory leak).\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Calibrating Na22 histograms based on Na22 calibration fit\n", + "hist_cal_bg_Na22 = calibrated_histogram(c_fit, acqs[4], BITS12)\n", + "hist_cal_bg_Na22.Scale(1/acqs[4].exposure) \n", + "hist_cal_src_Na22 = calibrated_histogram(c_fit, acqs[3], BITS12)\n", + "hist_cal_src_Na22.Scale(1/acqs[3].exposure) \n", + "hist_cal_clean_Na22 = hist_cal_src_Na22.Clone(\"Calibrated signal no BG\")\n", + "hist_cal_clean_Na22.Add(hist_cal_bg_Na22, -1)\n", + "\n", + "\n", + "# Plot the Cs-137 histograms\n", + "if ROOT.gROOT.FindObject('cv2'):\n", + " ROOT.gROOT.FindObject('cv2').Close()\n", + "cv2 = ROOT.TCanvas(\"cv2\", \"cv2\", 1600, 800)\n", + "cv2.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv2.cd(1)\n", + "hist_cal_bg_Na22.SetLineColor(colors[0])\n", + "hist_cal_src_Na22.SetLineColor(colors[1])\n", + "lg1.AddEntry(hist_cal_bg_Na22, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_cal_src_Na22, r\"Signal ^{22}Na\", \"l\")\n", + "hist_cal_src_Na22.Draw(\"hist\")\n", + "hist_cal_bg_Na22.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv2.cd(1).SetLogy()\n", + "#hist_cal_bg.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv2.cd(2)\n", + "hist_cal_clean_Na22.SetLineColor(colors[2])\n", + "hist_cal_clean_Na22.Draw(\"hist\")\n", + "cv2.cd(2).SetLogy()\n", + "\n", + "cv2.Draw()\n", + "\n", + "#Calculate energy resolution\n", + "res_Na22 = energy_resolution(hist_cal_clean_Na22, [(0.13, 0.6), (1.1, 1.5), (1.5 , 2.1)], Na22_MeV)\n", + "print(res_Na22)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dc817d8-9e6c-45c6-8045-f89abe669bb2", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ab79b628cbba92fdb21acd52c34a2fb6ddfe9461 Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Tue, 31 Mar 2026 17:01:37 +0200 Subject: [PATCH 16/18] Energy calibration function --- analysis/calibration.py | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/analysis/calibration.py b/analysis/calibration.py index 3fca9f5..c680b9a 100644 --- a/analysis/calibration.py +++ b/analysis/calibration.py @@ -4,13 +4,13 @@ def calibration_fit(histogram, energy_ranges, energies): """Function to create a linear calibration fit based on a histogram. Returns slope and constant of linear fit. Inputs: Histogram to base calibration on. - Ranges within which the peaks of the histogram are located. + Ranges within which the peaks of the histogram are located, input as a list if tuples. AT LEAST 2 points. Known energies of these peaks, in MeV.""" channels = [] for i in range(len(energy_ranges)): cal_fit=ROOT.TF1("cal_fit_" + str(i), "gaus", energy_ranges[i][0], energy_ranges[i][1]) - hist.Fit(cal_fit, "R+S") + histogram.Fit(cal_fit, "R+S") channels.append(cal_fit.GetParameter('Mean')) channels = np.array(channels, dtype='float64') @@ -18,7 +18,7 @@ def calibration_fit(histogram, energy_ranges, energies): graph = ROOT.TGraph(len(channels), channels, energies) fit = ROOT.TF1("calib", "pol1", min(channels), max(channels)) - graph.Fit(f) + graph.Fit(fit) a = fit.GetParameter(1) b = fit.GetParameter(0) @@ -43,10 +43,24 @@ def calibrated_histogram(linear_fit, acquisition, n_of_bins): def calibrated_acquisition(linear_fit, acquisition, n_of_bins): a = linear_fit[0] b = linear_fit[1] - print(a, b) data_cal = a * (acquisition['s']/len(acquisition.active_chs)) + b emax = a * n_of_bins + b emin = b - return(data_cal, emin, emax) \ No newline at end of file + return(data_cal, emin, emax) + + +def energy_resolution(hist, peak_ranges, peak_energies): + """Calculates energy resolution. Input peak_ranges as a list of tuples. + Outputs a list of resolutions for the different energies.""" + resolutions = [] + for i in range (len(peak_ranges)): + resolution_fit = ROOT.TF1("res_fit_" + str(i), "gaus", peak_ranges[i][0], peak_ranges[i][1]) + hist.Fit(resolution_fit, "R+S") + + std_dev = resolution_fit.GetParameter('Sigma') + resolution = (2.355 * std_dev)/peak_energies[i] + resolutions.append(resolution) + + return resolutions \ No newline at end of file From 62d6a0722e315967f167f6c5959c3073831d2256 Mon Sep 17 00:00:00 2001 From: Oscar Rosero Date: Wed, 1 Apr 2026 11:51:59 +0200 Subject: [PATCH 17/18] SIPHRA controller finished --- siphractrl/d2a_lib.py | 2 +- siphractrl/regs_bit_structure.py | 2 +- siphractrl/siphra_controller.py | 151 ++++++++++++++++++------------- 3 files changed, 89 insertions(+), 66 deletions(-) diff --git a/siphractrl/d2a_lib.py b/siphractrl/d2a_lib.py index 1dee38e..2dce62d 100644 --- a/siphractrl/d2a_lib.py +++ b/siphractrl/d2a_lib.py @@ -65,7 +65,7 @@ def compareUpTo(a, b, len): class D2a: ''' - Contains all fucntions required to interface with hardware devices through OS. + Contains all functions required to interface with hardware devices through OS. ''' def __init__(self, uio='/dev/uio0', spi='/dev/spi'): try: diff --git a/siphractrl/regs_bit_structure.py b/siphractrl/regs_bit_structure.py index 2af9b8a..8fa76e1 100644 --- a/siphractrl/regs_bit_structure.py +++ b/siphractrl/regs_bit_structure.py @@ -120,7 +120,7 @@ 'readout_en_int_hold_start' / Flag, ) -reg_str_lst = [CHANNEL, +reg_str_lst = [*[CHANNEL]*16, SUM_CHANNEL, CHANNEL_CONFIG, CHANNEL_CONTROL, diff --git a/siphractrl/siphra_controller.py b/siphractrl/siphra_controller.py index 6819763..1ec194d 100644 --- a/siphractrl/siphra_controller.py +++ b/siphractrl/siphra_controller.py @@ -1,3 +1,5 @@ +from unittest import case + from .d2a_lib import * from .regs_bit_structure import * from collections import namedtuple @@ -42,57 +44,37 @@ def set_param(self, param_name, value, current_content): reg_content[param_name] = value return self.structure.build(reg_content) - -class SIPHRARegMap: - def __init__(self): - self._registers = {} - - for i in range(1,17): - self._registers[f"ch{i}"] = SIPHRARegister(i, CHANNEL) - - self._registers["chsum"] = SIPHRARegister(0x10, SUM_CHANNEL) - self._registers["ch_config"] = SIPHRARegister(0x11, CHANNEL_CONFIG) - - def __getitem__(self, name): - return self._registers[name] - - def __getattr__(self, name): - return self._registers[name] - - def __iter__(self): - return iter(self._registers.values()) - - def __len__(self): - return len(self._registers) - - def __contains__(self, name): - return name in self._registers - - def items(self): - return self._registers.items() - - def keys(self): - return self._registers.keys() - - def values(self): - return self._registers.values() - - class SIPHRA: - def __init__(self, d2a: D2a): - self._d2a = d2a - self._regs = SIPHRARegMap() - - def get_reg_value(self, reg_name, chip='A'): - addr = self._regs[reg_name].addr - reg_value = self._d2a.readSIPHRA(addr, chip)[SIPHRA_RETURN_SIZE - REG_SIZE:] - return reg_value - - def read_register(self, reg_name, chip='A'): - reg_value = self.get_reg_value(reg_name, chip) - strct = self._regs[reg_name].structure - parsed_struct = strct.parse(reg_value) - return parsed_struct + def __init__(self): + self._d2a = D2a() + self._regs = {} + + for i in range(1, 17): + self._regs[f"ch{i}"] = SIPHRARegister(i, CHANNEL) + + self._regs["ch17"] = SIPHRARegister(0x10, SUM_CHANNEL) + self._regs["ch_config"] = SIPHRARegister(0x11, CHANNEL_CONFIG) + self._regs["ch_control"] = SIPHRARegister(0x12, CHANNEL_CONTROL) + self._regs["adc_config"] = SIPHRARegister(0x13, ADC_CONFIG) + self._regs["cal_dac"] = SIPHRARegister(0x14, CAL_DAC) + self._regs["pd_modules"] = SIPHRARegister(0x15, PD_MODULES) + self._regs["cal_ctrl"] = SIPHRARegister(0x16, CAL_CTRL) + self._regs["readout_list"] = SIPHRARegister(0x17, READOUT_FIXED_LIST) + self._regs["readout_mode"] = SIPHRARegister(0x18, READOUT_MODE) + + def _resolve_reg_id(self, reg_id: str | int) -> tuple[str, int]: + """Resolves a register name or address to a (name, addr) tuple.""" + match reg_id: + case str(): + if reg_id in self._regs: + return reg_id, self._regs[reg_id].addr + case int(): + for name, reg in self._regs.items(): + if reg.addr == reg_id: + return name, reg_id + case _: + raise TypeError(f"reg_id must be str or int, got {type(reg_id).__name__}") + raise ValueError(f"{reg_id!r} is not a valid register id") def _find_reg_containing_param(self, parameter, ch: int=0): ''' @@ -101,26 +83,67 @@ def _find_reg_containing_param(self, parameter, ch: int=0): :param ch: If the parameter is in one of the 16+1 channel addresses, this argument is used for disambiguation. ``ch`` is a number between 1 and 16 if the parameter belongs to one of the ``ctrl_ch`` registers; it is 17 if the parameter belongs to the summing channel control register, and is defaulted to 0 if the parameter belongs to any other register. :return: The address of the register containing the given parameter. ''' - reg_addr = None - if not 0 < ch < 17: + addr = None + name = None + if not 0 <= ch <= 17: raise ValueError(f"Channel {ch} is out of range. Use channels 1-16 and 17 for summing channel") if ch == 0: # Not a channel register - for reg in self._regs: + for n, reg in self._regs.items(): if parameter in reg: - reg_addr = reg.addr + name = n + addr = reg.addr else: # Channel register - # Verify that the given register exists - register = self._regs[f"ch{ch}"] - if parameter in register: reg_addr = register.addr - if not reg_addr: + # Verify that the given field exists + reg = self._regs[f"ch{ch}"] + if parameter in reg: + addr = reg.addr + name = f"ch{ch}" + if not addr: raise NameError(f"Parameter {parameter} does not exist or the register containing it is not implemented.") - return reg_addr - + return name, addr + + def get_reg_value(self, reg_id, chip='A'): + _, addr = self._resolve_reg_id(reg_id) + return self._d2a.readSIPHRA(addr, chip) + + def read_register(self, reg_id, chip='A'): + name, addr = self._resolve_reg_id(reg_id) + reg_value = self._d2a.readSIPHRA(addr, chip) + return self._regs[name].parse(reg_value) + + def write_register(self, reg_id, value, chip='A'): + _, addr = self._resolve_reg_id(reg_id) + try: + self._d2a.writeSIPHRAwithCheck(addr, chip, value) + except Exception as e: + print(e) + return -1 + return 0 + + def read_param(self, parameter, ch=0, reg_id=None, chip='A'): + '''``reg_id`` can be the register name or its address''' + if reg_id: + name, addr = self._resolve_reg_id(reg_id) + if not parameter in _regs[name]: + raise NameError(f"Parameter {parameter} does not exist in register {reg_id}.") + else: + name, addr = self._find_reg_containing_param(parameter, ch) + return self.read_register(name, chip=chip)[parameter] - def read_param(self, parameter, ch=0, reg_name=None, chip='A'): - if reg_name: - reg_addr = self._regs[reg_name].addr + def write_param(self, parameter, value, ch=0, reg_id=None, chip='A'): + if reg_id: + name, addr = self._resolve_reg_id(reg_id) + if not parameter in _regs[name]: + raise NameError(f"Parameter {parameter} does not exist in register {reg_id}.") else: - reg_addr = self._find_reg_containing_param(parameter, ch) + name, addr = self._find_reg_containing_param(parameter, ch) + + reg = self._regs[name] + old_value = self.get_reg_value(reg_id, chip=chip) + new_value = reg.set_param(parameter, value, old_value) + self.write_register(name, new_value, chip=chip) + + + From f84f8feba9b9dfeb4c8411d18674a10f65ac5909 Mon Sep 17 00:00:00 2001 From: JohannaBark Date: Wed, 1 Apr 2026 11:58:58 +0200 Subject: [PATCH 18/18] Testing calibration and energy resolution functions --- notebooks/Calibration_EnergyResolution.ipynb | 322 ++++++++++++++----- 1 file changed, 246 insertions(+), 76 deletions(-) diff --git a/notebooks/Calibration_EnergyResolution.ipynb b/notebooks/Calibration_EnergyResolution.ipynb index 4b7ac97..e32e9af 100644 --- a/notebooks/Calibration_EnergyResolution.ipynb +++ b/notebooks/Calibration_EnergyResolution.ipynb @@ -114,21 +114,21 @@ "text/html": [ "\n", "\n", - "
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\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Calibration based on the 3 peaks of the Na-22 spectrum\n", + "hist = hists['Am-241_clean']\n", + "energy_ranges = [(0, 1), (5, 30)]\n", + "energies = [0, Am241_MeV]\n", + "\n", + "c_fit = calibration_fit(hist, energy_ranges, energies)\n", + "print(c_fit)\n", + "\n", + "#Calibrating Na22 histograms based on Na22 calibration fit\n", + "hist_cal_bg_Am241 = calibrated_histogram(c_fit, acqs[8], BITS12)\n", + "hist_cal_bg_Am241.Scale(1/acqs[8].exposure) \n", + "hist_cal_src_Am241 = calibrated_histogram(c_fit, acqs[7], BITS12)\n", + "hist_cal_src_Am241.Scale(1/acqs[7].exposure) \n", + "hist_cal_clean_Am241 = hist_cal_src_Am241.Clone(\"Calibrated signal no BG\")\n", + "hist_cal_clean_Am241.Add(hist_cal_bg_Am241, -1)\n", + "\n", + "\n", + "# Plot the Cs-137 histograms\n", + "if ROOT.gROOT.FindObject('cv4'):\n", + " ROOT.gROOT.FindObject('cv4').Close()\n", + "cv4 = ROOT.TCanvas(\"cv4\", \"cv4\", 1600, 800)\n", + "cv4.Divide(2,1)\n", + "\n", + "lg1 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv4.cd(1)\n", + "hist_cal_bg_Am241.SetLineColor(colors[0])\n", + "hist_cal_src_Am241.SetLineColor(colors[1])\n", + "lg1.AddEntry(hist_cal_bg_Am241, \"Background\", \"l\")\n", + "lg1.AddEntry(hist_cal_src_Am241, r\"Signal ^{241}Am\", \"l\")\n", + "hist_cal_src_Am241.Draw(\"hist\")\n", + "hist_cal_bg_Am241.Draw(\"sames hist\")\n", + "lg1.Draw()\n", + "cv4.cd(1).SetLogy()\n", + "#hist_cal_bg.GetYaxis().SetRangeUser(0,1e4)\n", + "\n", + "\n", + "lg2 = ROOT.TLegend(0.48, 0.71, 0.75, 0.83)\n", + "cv4.cd(2)\n", + "hist_cal_clean_Am241.SetLineColor(colors[2])\n", + "hist_cal_clean_Am241.Draw(\"hist\")\n", + "cv4.cd(2).SetLogy()\n", + "\n", + "cv4.Draw()\n", + "\n", + "#Calculate energy resolution\n", + "res_Am241 = energy_resolution(hist_cal_clean_Am241, [(0.01, 0.09)], [Am241_MeV])\n", + "print(res_Am241)\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": null,