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This commit is contained in:
Vasily Chernov
2025-03-03 15:09:44 +03:00
parent daa8b78b8c
commit 8062d8de2a
14 changed files with 29061 additions and 317382 deletions
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.gitignore vendored
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batch
batch*
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/.idea/

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Project.toml Normal file
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[deps]
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TrappingToFit.md Normal file
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## Получение треппинга
1. Получаю события из симуляции нозика. Пример таблицы:
```
id E theta theta_start colNum L state
164063 11044.8 22.3165 149.789 26 4191.84 ACCEPTED
```
из данных также беру общее количество событий PASS (не попавших в ловушку
и идущих в спектр)
2. Для каждой энергии беру колону `E` и вычитаю ее из этой энергии. По полученным данным строю 2 гистограммы:
1. 0..50 эВ с шагом 1
2. 0..7500 эВ с шагом 100
3. Нормирую каждую гистограмму на количество PASS для данной энергии
4. По полученным гистограммам делаю 2 сеточные функции с линейной интерполяцией
f(E, delta)
- если дельта < 50, использую точную сетку, если больше - грубую
5. Использую полученный интерполятор в качестве функции треппига.
Нозик передает в качестве агрументов ei, ef. В интерполятор идет
f(ei, ei - ef)

4
gather.sh
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#!/bin/bash
# Script parameters
BATCH_DIRECTORY="batch"
VOLTAGES=("11000.0" "12000.0" "14000.0" "16000.0" "18000.0")
BATCH_DIRECTORY="batch-02"
VOLTAGES=("8000.0")
mkdir -p $BATCH_DIRECTORY

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notebooks/.ipynb_checkpoints/cross-sections-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-08T18:49+0000",
"start_time": "2018-04-08T18:49+0000"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b3666d93-1a9f-47f6-b272-8c9cde5eb9eb",
"version_major": 2,
"version_minor": 0
},
"method": "display_data"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0e65aaab-812e-45f3-98df-43ed93255c5e",
"version_major": 2,
"version_minor": 0
},
"method": "display_data"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%classpath add jar ../build/install/trapping/lib/commons-math3-3.6.1.jar\n",
"%classpath add jar ../build/install/trapping/lib/trapping-dev.jar"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-08T18:58+0000",
"start_time": "2018-04-08T18:58+0000"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e62373b-9e28-4669-937f-06f5c908a4bd",
"version_major": 2,
"version_minor": 0
},
"method": "display_data"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"scatter = inr.numass.trapping.Scatter.INSTANCE\n",
"\n",
"def e = (1..20) // energy in keV\n",
"\n",
"factor = 1e-22\n",
"sigmaEl = e.collect{scatter.sigmael(it*1000)/factor}\n",
"sigmaIon = e.collect{scatter.sigmaion(it*1000)/factor}\n",
"sigmaExc = e.collect{scatter.sigmaexc(it*1000)/factor}\n",
"\n",
"def plot = new Plot(logY: true, yLogBase: 10)\n",
"plot << new Line(x: e, y: sigmaEl, displayName: \"Elastic\")\n",
"plot << new Line(x: e, y: sigmaIon, displayName: \"Ionization\")\n",
"plot << new Line(x: e, y: sigmaExc, displayName: \"Excitation\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"E(keV)\telastic\tion\texc\n",
"12.0\t0.43948540252301355\t2.7362696996880764\t2.2754271467658227\n",
"12.1\t0.4360219815420227\t2.716347129996631\t2.259247553470829\n",
"12.2\t0.4326153394877464\t2.6967291910800784\t2.2433117665045716\n",
"12.3\t0.42926409223681455\t2.6774088086825643\t2.227614186457129\n",
"12.4\t0.42596690029168804\t2.6583791281775273\t2.212149386181278\n",
"12.5\t0.4227224669965498\t2.639633506055748\t2.1969121041685504\n",
"12.6\t0.41952953683810995\t2.621165501807934\t2.1818972382301722\n",
"12.7\t0.4163868938266485\t2.602968870180603\t2.1670998394665926\n",
"12.8\t0.4132933599529065\t2.585037553785318\t2.1525151065102723\n",
"12.9\t0.4102477937167139\t2.567365676042546\t2.138138380027344\n",
"13.0\t0.4072490887234942\t2.5499475344425586\t2.123965137464613\n",
"13.1\t0.4042961723450181\t2.532777594106803\t2.1099909880291814\n",
"13.2\t0.40138800444100226\t2.515850481634202\t2.096211667888705\n",
"13.3\t0.3985235761383543\t2.4991609792177183\t2.082623035581028\n",
"13.4\t0.3957019086650494\t2.4827040190174023\t2.069221067622563\n",
"13.5\t0.39292205223581\t2.466474677776929\t2.0560018543054204\n",
"13.6\t0.39018308498692494\t2.4504681716713828\t2.042961595673853\n",
"13.7\t0.3874841119576961\t2.434679851374739\t2.0300965976711156\n",
"13.8\t0.3848242641161482\t2.419105197336177\t2.0174032684483527\n",
"13.9\t0.3822026974267757\t2.403739815254919\t2.00487811482759\n",
"14.0\t0.3796185919582249\t2.3885794317439313\t1.9925177389113418\n",
"14.1\t0.3770711510289289\t2.373619890173301\t1.9803188348317768\n",
"14.2\t0.3745596003888268\t2.358857146684646\t1.9682781856327505\n",
"14.3\t0.3720831874354002\t2.3442872663683785\t1.9563926602783916\n",
"14.4\t0.36964118046236066\t2.32990641959609\t1.9446592107822769\n",
"14.5\t0.36723286793941123\t2.315710878500726\t1.9330748694515214\n",
"14.6\t0.3648575578215951\t2.3016970135976464\t1.9216367462404509\n",
"14.7\t0.36251457688682004\t2.287861290540006\t1.910342026208781\n",
"14.8\t0.3602032701002296\t2.274200267002247\t1.8991879670794969\n",
"14.9\t0.357923000004158\t2.26071058968582\t1.8881718968918926\n",
"15.0\t0.3556731461324769\t2.2473889914415754\t1.8772912117454528\n",
"15.1\t0.353453104448203\t2.234232288503513\t1.8665433736304802\n",
"15.2\t0.35126228680329497\t2.221237377828907\t1.8559259083415982\n",
"15.3\t0.3491001204196282\t2.2084012345400286\t1.845436403470438\n",
"15.4\t0.3469660473901797\t2.1957209094629615\t1.835072506474014\n",
"15.5\t0.3448595241995173\t2.1831935267592297\t1.824831922815469\n",
"15.6\t0.3427800212627204\t2.170816281646157\t1.8147124141740314\n",
"15.7\t0.34072702248191666\t2.158586438202101\t1.8047117967211865\n",
"15.8\t0.33870002481965195\t2.1465013272528792\t1.7948279394602165\n",
"15.9\t0.3366985378883526\t2.134558344335901\t1.7850587626263867\n",
"16.0\t0.33472208355517563\t2.122754947738674\t1.7754022361452126\n",
"16.1\t0.33277019556158083\t2.111088656608532\t1.7658563781463463\n",
"16.2\t0.33084241915698676\t2.0995570491305764\t1.7564192535307461\n",
"16.3\t0.32893831074590485\t2.0881577607709536\t1.7470889725889123\n",
"16.4\t0.3270574375479805\t2.0768884825827687\t1.7378636896680617\n",
"16.5\t0.3251993772703896\t2.0657469595720115\t1.7287416018862318\n",
"16.6\t0.32336371779207285\t2.054730989121034\t1.7197209478913829\n",
"16.7\t0.3215500568593091\t2.043838419467228\t1.7108000066636726\n",
"16.8\t0.31975800179215863\t2.0330671482346325\t1.7019770963591483\n",
"16.9\t0.3179871692013229\t2.022415121016355\t1.693250573193194\n",
"17.0\t0.3162371847149949\t2.011880330005736\t1.684618830362136\n",
"17.1\t0.3145076827152896\t2.0014608126743285\t1.6760802970014965\n",
"17.2\t0.31279830608386505\t1.991154650494809\t1.6676334371794341\n",
"17.3\t0.31110870595636253\t1.9809599677070577\t1.6592767489239986\n",
"17.4\t0.3094385414853119\t1.9708749301257074\t1.6510087632828718\n",
"17.5\t0.3077874796111622\t1.9608977439875288\t1.6428280434143254\n",
"17.6\t0.30615519484111486\t1.9510266548371218\t1.6347331837082066\n",
"17.7\t0.3045413690354524\t1.9412599464494087\t1.6267228089357717\n",
"17.8\t0.3029456912010658\t1.9315959397875393\t1.6187955734272899\n",
"17.9\t0.3013678572919011\t1.9220329919948316\t1.6109501602763423\n",
"18.0\t0.29980757001605496\t1.9125694954194674\t1.603185280569817\n",
"18.1\t0.29826453864926233\t1.9032038766707016\t1.5954996726426307\n",
"18.2\t0.29673847885453164\t1.8939345957053948\t1.5878921013562524\n",
"18.3\t0.29522911250768985\t1.8847601449437406\t1.5803613574001418\n",
"18.4\t0.2937361675286164\t1.8756790484131034\t1.5729062566152603\n",
"18.5\t0.2922593777179467\t1.866689860918917\t1.565525639338836\n"
]
},
{
"data": {
"text/plain": [
"null"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"println \"E(keV)\\telastic\\tion\\texc\"\n",
"for(d = 12.0; d < 18.6; d += 0.1){\n",
" def sigmaEl = scatter.sigmael(d*1000)/factor\n",
" def sigmaIon = scatter.sigmaion(d*1000)/factor\n",
" def sigmaExc = scatter.sigmaexc(d*1000)/factor\n",
" println \"$d\\t$sigmaEl\\t$sigmaIon\\t$sigmaExc\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b29b147e-ab0d-473b-b9db-909013996ad7",
"version_major": 2,
"version_minor": 0
},
"method": "display_data"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import com.twosigma.beakerx.table.*\n",
"\n",
"def list = []\n",
" \n",
"for(d = 12.0; d < 18.6; d += 0.1){\n",
" list << [\n",
" \"x\": d,\n",
" \"el\": scatter.sigmael(d*1000)/factor,\n",
" \"ion\": scatter.sigmaion(d*1000)/factor,\n",
" \"exc\": scatter.sigmaexc(d*1000)/factor \n",
" ]\n",
"}\n",
"beakerx[\"table\"] = new TableDisplay(list)\n",
"list"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'x': 12.0,\n",
" 'el': 0.43948540252301355,\n",
" 'ion': 2.7362696996880764,\n",
" 'exc': 2.2754271467658227},\n",
" {'x': 12.1,\n",
" 'el': 0.4360219815420227,\n",
" 'ion': 2.716347129996631,\n",
" 'exc': 2.259247553470829},\n",
" {'x': 12.2,\n",
" 'el': 0.4326153394877464,\n",
" 'ion': 2.6967291910800784,\n",
" 'exc': 2.2433117665045716},\n",
" {'x': 12.3,\n",
" 'el': 0.42926409223681455,\n",
" 'ion': 2.6774088086825643,\n",
" 'exc': 2.227614186457129},\n",
" {'x': 12.4,\n",
" 'el': 0.42596690029168804,\n",
" 'ion': 2.6583791281775273,\n",
" 'exc': 2.212149386181278},\n",
" {'x': 12.5,\n",
" 'el': 0.4227224669965498,\n",
" 'ion': 2.639633506055748,\n",
" 'exc': 2.1969121041685504},\n",
" {'x': 12.6,\n",
" 'el': 0.41952953683810995,\n",
" 'ion': 2.621165501807934,\n",
" 'exc': 2.1818972382301722},\n",
" {'x': 12.7,\n",
" 'el': 0.4163868938266485,\n",
" 'ion': 2.602968870180603,\n",
" 'exc': 2.1670998394665926},\n",
" {'x': 12.8,\n",
" 'el': 0.4132933599529065,\n",
" 'ion': 2.585037553785318,\n",
" 'exc': 2.1525151065102723},\n",
" {'x': 12.9,\n",
" 'el': 0.4102477937167139,\n",
" 'ion': 2.567365676042546,\n",
" 'exc': 2.138138380027344},\n",
" {'x': 13.0,\n",
" 'el': 0.4072490887234942,\n",
" 'ion': 2.5499475344425586,\n",
" 'exc': 2.123965137464613},\n",
" {'x': 13.1,\n",
" 'el': 0.4042961723450181,\n",
" 'ion': 2.532777594106803,\n",
" 'exc': 2.1099909880291814},\n",
" {'x': 13.2,\n",
" 'el': 0.40138800444100226,\n",
" 'ion': 2.515850481634202,\n",
" 'exc': 2.096211667888705},\n",
" {'x': 13.3,\n",
" 'el': 0.3985235761383543,\n",
" 'ion': 2.4991609792177183,\n",
" 'exc': 2.082623035581028},\n",
" {'x': 13.4,\n",
" 'el': 0.3957019086650494,\n",
" 'ion': 2.4827040190174023,\n",
" 'exc': 2.069221067622563},\n",
" {'x': 13.5,\n",
" 'el': 0.39292205223581,\n",
" 'ion': 2.466474677776929,\n",
" 'exc': 2.0560018543054204},\n",
" {'x': 13.6,\n",
" 'el': 0.39018308498692494,\n",
" 'ion': 2.4504681716713828,\n",
" 'exc': 2.042961595673853},\n",
" {'x': 13.7,\n",
" 'el': 0.3874841119576961,\n",
" 'ion': 2.434679851374739,\n",
" 'exc': 2.0300965976711156},\n",
" {'x': 13.8,\n",
" 'el': 0.3848242641161482,\n",
" 'ion': 2.419105197336177,\n",
" 'exc': 2.0174032684483527},\n",
" {'x': 13.9,\n",
" 'el': 0.3822026974267757,\n",
" 'ion': 2.403739815254919,\n",
" 'exc': 2.00487811482759},\n",
" {'x': 14.0,\n",
" 'el': 0.3796185919582249,\n",
" 'ion': 2.3885794317439313,\n",
" 'exc': 1.9925177389113418},\n",
" {'x': 14.1,\n",
" 'el': 0.3770711510289289,\n",
" 'ion': 2.373619890173301,\n",
" 'exc': 1.9803188348317768},\n",
" {'x': 14.2,\n",
" 'el': 0.3745596003888268,\n",
" 'ion': 2.358857146684646,\n",
" 'exc': 1.9682781856327505},\n",
" {'x': 14.3,\n",
" 'el': 0.3720831874354002,\n",
" 'ion': 2.3442872663683785,\n",
" 'exc': 1.9563926602783916},\n",
" {'x': 14.4,\n",
" 'el': 0.36964118046236066,\n",
" 'ion': 2.32990641959609,\n",
" 'exc': 1.9446592107822769},\n",
" {'x': 14.5,\n",
" 'el': 0.36723286793941123,\n",
" 'ion': 2.315710878500726,\n",
" 'exc': 1.9330748694515214},\n",
" {'x': 14.6,\n",
" 'el': 0.3648575578215951,\n",
" 'ion': 2.3016970135976464,\n",
" 'exc': 1.9216367462404509},\n",
" {'x': 14.7,\n",
" 'el': 0.36251457688682004,\n",
" 'ion': 2.287861290540006,\n",
" 'exc': 1.910342026208781},\n",
" {'x': 14.8,\n",
" 'el': 0.3602032701002296,\n",
" 'ion': 2.274200267002247,\n",
" 'exc': 1.8991879670794969},\n",
" {'x': 14.9,\n",
" 'el': 0.357923000004158,\n",
" 'ion': 2.26071058968582,\n",
" 'exc': 1.8881718968918926},\n",
" {'x': 15.0,\n",
" 'el': 0.3556731461324769,\n",
" 'ion': 2.2473889914415754,\n",
" 'exc': 1.8772912117454528},\n",
" {'x': 15.1,\n",
" 'el': 0.353453104448203,\n",
" 'ion': 2.234232288503513,\n",
" 'exc': 1.8665433736304802},\n",
" {'x': 15.2,\n",
" 'el': 0.35126228680329497,\n",
" 'ion': 2.221237377828907,\n",
" 'exc': 1.8559259083415982},\n",
" {'x': 15.3,\n",
" 'el': 0.3491001204196282,\n",
" 'ion': 2.2084012345400286,\n",
" 'exc': 1.845436403470438},\n",
" {'x': 15.4,\n",
" 'el': 0.3469660473901797,\n",
" 'ion': 2.1957209094629615,\n",
" 'exc': 1.835072506474014},\n",
" {'x': 15.5,\n",
" 'el': 0.3448595241995173,\n",
" 'ion': 2.1831935267592297,\n",
" 'exc': 1.824831922815469},\n",
" {'x': 15.6,\n",
" 'el': 0.3427800212627204,\n",
" 'ion': 2.170816281646157,\n",
" 'exc': 1.8147124141740314},\n",
" {'x': 15.7,\n",
" 'el': 0.34072702248191666,\n",
" 'ion': 2.158586438202101,\n",
" 'exc': 1.8047117967211865},\n",
" {'x': 15.8,\n",
" 'el': 0.33870002481965195,\n",
" 'ion': 2.1465013272528792,\n",
" 'exc': 1.7948279394602165},\n",
" {'x': 15.9,\n",
" 'el': 0.3366985378883526,\n",
" 'ion': 2.134558344335901,\n",
" 'exc': 1.7850587626263867},\n",
" {'x': 16.0,\n",
" 'el': 0.33472208355517563,\n",
" 'ion': 2.122754947738674,\n",
" 'exc': 1.7754022361452126},\n",
" {'x': 16.1,\n",
" 'el': 0.33277019556158083,\n",
" 'ion': 2.111088656608532,\n",
" 'exc': 1.7658563781463463},\n",
" {'x': 16.2,\n",
" 'el': 0.33084241915698676,\n",
" 'ion': 2.0995570491305764,\n",
" 'exc': 1.7564192535307461},\n",
" {'x': 16.3,\n",
" 'el': 0.32893831074590485,\n",
" 'ion': 2.0881577607709536,\n",
" 'exc': 1.7470889725889123},\n",
" {'x': 16.4,\n",
" 'el': 0.3270574375479805,\n",
" 'ion': 2.0768884825827687,\n",
" 'exc': 1.7378636896680617},\n",
" {'x': 16.5,\n",
" 'el': 0.3251993772703896,\n",
" 'ion': 2.0657469595720115,\n",
" 'exc': 1.7287416018862318},\n",
" {'x': 16.6,\n",
" 'el': 0.32336371779207285,\n",
" 'ion': 2.054730989121034,\n",
" 'exc': 1.7197209478913829},\n",
" {'x': 16.7,\n",
" 'el': 0.3215500568593091,\n",
" 'ion': 2.043838419467228,\n",
" 'exc': 1.7108000066636726},\n",
" {'x': 16.8,\n",
" 'el': 0.31975800179215863,\n",
" 'ion': 2.0330671482346325,\n",
" 'exc': 1.7019770963591483},\n",
" {'x': 16.9,\n",
" 'el': 0.3179871692013229,\n",
" 'ion': 2.022415121016355,\n",
" 'exc': 1.693250573193194},\n",
" {'x': 17.0,\n",
" 'el': 0.3162371847149949,\n",
" 'ion': 2.011880330005736,\n",
" 'exc': 1.684618830362136},\n",
" {'x': 17.1,\n",
" 'el': 0.3145076827152896,\n",
" 'ion': 2.0014608126743285,\n",
" 'exc': 1.6760802970014965},\n",
" {'x': 17.2,\n",
" 'el': 0.31279830608386505,\n",
" 'ion': 1.991154650494809,\n",
" 'exc': 1.6676334371794341},\n",
" {'x': 17.3,\n",
" 'el': 0.31110870595636253,\n",
" 'ion': 1.9809599677070577,\n",
" 'exc': 1.6592767489239986},\n",
" {'x': 17.4,\n",
" 'el': 0.3094385414853119,\n",
" 'ion': 1.9708749301257074,\n",
" 'exc': 1.6510087632828718},\n",
" {'x': 17.5,\n",
" 'el': 0.3077874796111622,\n",
" 'ion': 1.9608977439875288,\n",
" 'exc': 1.6428280434143254},\n",
" {'x': 17.6,\n",
" 'el': 0.30615519484111486,\n",
" 'ion': 1.9510266548371218,\n",
" 'exc': 1.6347331837082066},\n",
" {'x': 17.7,\n",
" 'el': 0.3045413690354524,\n",
" 'ion': 1.9412599464494087,\n",
" 'exc': 1.6267228089357717},\n",
" {'x': 17.8,\n",
" 'el': 0.3029456912010658,\n",
" 'ion': 1.9315959397875393,\n",
" 'exc': 1.6187955734272899},\n",
" {'x': 17.9,\n",
" 'el': 0.3013678572919011,\n",
" 'ion': 1.9220329919948316,\n",
" 'exc': 1.6109501602763423},\n",
" {'x': 18.0,\n",
" 'el': 0.29980757001605496,\n",
" 'ion': 1.9125694954194674,\n",
" 'exc': 1.603185280569817},\n",
" {'x': 18.1,\n",
" 'el': 0.29826453864926233,\n",
" 'ion': 1.9032038766707016,\n",
" 'exc': 1.5954996726426307},\n",
" {'x': 18.2,\n",
" 'el': 0.29673847885453164,\n",
" 'ion': 1.8939345957053948,\n",
" 'exc': 1.5878921013562524},\n",
" {'x': 18.3,\n",
" 'el': 0.29522911250768985,\n",
" 'ion': 1.8847601449437406,\n",
" 'exc': 1.5803613574001418},\n",
" {'x': 18.4,\n",
" 'el': 0.2937361675286164,\n",
" 'ion': 1.8756790484131034,\n",
" 'exc': 1.5729062566152603},\n",
" {'x': 18.5,\n",
" 'el': 0.2922593777179467,\n",
" 'ion': 1.866689860918917,\n",
" 'exc': 1.565525639338836}]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%python\n",
"from beakerx.object import beakerx\n",
"import pandas as pd\n",
"beakerx.table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Groovy",
"language": "groovy",
"name": "groovy"
},
"language_info": {
"codemirror_mode": "groovy",
"file_extension": ".groovy",
"mimetype": "",
"name": "Groovy",
"nbconverter_exporter": "",
"version": "2.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

332
notebooks/.ipynb_checkpoints/trap_spectrum-checkpoint.ipynb Normal file
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@@ -0,0 +1,332 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-2.32.0.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"from scipy.optimize import curve_fit\n",
"\n",
"\n",
"import plotly\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"import plotly.graph_objs as go\n",
"import plotly.io as pio\n",
"\n",
"init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['id', 'E', 'theta', 'theta_start', 'colNum', 'L', 'state'], dtype='object')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trap18 = pd.read_table(\"../batch-03-06-24/0/trap[18000.0].out\", sep=\"\\\\s+\",comment=\"#\")\n",
"trap16 = pd.read_table(\"../batch-03-06-24/0/trap[16000.0].out\", sep=\"\\\\s+\",comment=\"#\")\n",
"trap14 = pd.read_table(\"../batch-03-06-24/0/trap[14000.0].out\", sep=\"\\\\s+\",comment=\"#\")\n",
"trap12 = pd.read_table(\"../batch-03-06-24/0/trap[12000.0].out\", sep=\"\\\\s+\",comment=\"#\")\n",
"trap18.columns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'trap11' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[3], line 14\u001b[0m\n\u001b[1;32m 12\u001b[0m hist14_plot \u001b[38;5;241m=\u001b[39m go\u001b[38;5;241m.\u001b[39mHistogram(x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m14000\u001b[39m \u001b[38;5;241m-\u001b[39m trap14\u001b[38;5;241m.\u001b[39mE, xbins\u001b[38;5;241m=\u001b[39mbins, opacity\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m, name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mE = 14 keV\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 13\u001b[0m hist12_plot \u001b[38;5;241m=\u001b[39m go\u001b[38;5;241m.\u001b[39mHistogram(x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m12000\u001b[39m \u001b[38;5;241m-\u001b[39m trap12\u001b[38;5;241m.\u001b[39mE, xbins\u001b[38;5;241m=\u001b[39mbins, opacity\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m, name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mE = 12 keV\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m hist11_plot \u001b[38;5;241m=\u001b[39m go\u001b[38;5;241m.\u001b[39mHistogram(x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m11000\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[43mtrap11\u001b[49m\u001b[38;5;241m.\u001b[39mE, xbins\u001b[38;5;241m=\u001b[39mbins, opacity\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m, name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mE = 11 keV\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mNameError\u001b[0m: name 'trap11' is not defined"
]
}
],
"source": [
"# bins = 60\n",
"\n",
"# hist18_plot = go.Histogram(x = 18000 - trap18.E, nbinsx=bins, opacity=0.5, name = \"E = 18 keV\")\n",
"# hist16_plot = go.Histogram(x = 16000 - trap16.E, nbinsx=bins, opacity=0.5, name = \"E = 16 keV\")\n",
"# hist14_plot = go.Histogram(x = 14000 - trap14.E, nbinsx=bins, opacity=0.5, name = \"E = 14 keV\")\n",
"# hist12_plot = go.Histogram(x = 12000 - trap12.E, nbinsx=bins, opacity=0.5, name = \"E = 12 keV\")\n",
"\n",
"bins = go.histogram.XBins(start=0, end=7500, size=100)\n",
"\n",
"hist18_plot = go.Histogram(x = 18000 - trap18.E, xbins=bins, opacity=0.3, name = \"E = 18 keV\")\n",
"hist16_plot = go.Histogram(x = 16000 - trap16.E, xbins=bins, opacity=0.3, name = \"E = 16 keV\")\n",
"hist14_plot = go.Histogram(x = 14000 - trap14.E, xbins=bins, opacity=0.3, name = \"E = 14 keV\")\n",
"hist12_plot = go.Histogram(x = 12000 - trap12.E, xbins=bins, opacity=0.3, name = \"E = 12 keV\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layout = go.Layout(\n",
" barmode='overlay',\n",
" xaxis=dict(\n",
" # range= [0,3950], \n",
" title=\"\\u03B5 (eV)\"\n",
" ),\n",
" yaxis=dict(title=\"N\"),\n",
" #legend=dict(orientation=\"h\"),\n",
")\n",
"\n",
"fig = go.Figure( data = [hist12_plot, hist14_plot, hist16_plot, hist18_plot], layout=layout )\n",
"\n",
"# pio.write_image(fig, \"images/spectra.pdf\")\n",
"\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"den = False\n",
"r = None#[300,3900]\n",
"bins = 40\n",
"\n",
"hist18 = np.histogram(a=18000 - trap18.E,range=r,bins=bins,density=den)\n",
"hist16 = np.histogram(a=16000 - trap16.E,range=r,bins=bins,density=den)\n",
"hist14 = np.histogram(a=14000 - trap14.E,range=r,bins=bins,density=den)\n",
"hist12 = np.histogram(a=12000 - trap12.E,range=r,bins=bins,density=den)\n",
"\n",
"x = (hist18[1] + np.roll(hist18[1],-1))[:-1]/2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hist18[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f(x, norm, exp_amp, exp_const):\n",
" return norm*( exp_amp * np.exp(-x / exp_const) + 1.0 + 4e-5*x)\n",
"\n",
"p_start = [1000, 2.0, 100]\n",
"\n",
"hist18_fit, _ = curve_fit(f=f, xdata= x, ydata=hist18[0], sigma=np.sqrt(hist18[0]),p0=p_start)\n",
"hist16_fit, _ = curve_fit(f=f, xdata= x, ydata=hist16[0], sigma=np.sqrt(hist16[0]),p0=p_start)\n",
"hist14_fit, _ = curve_fit(f=f, xdata= x, ydata=hist14[0], sigma=np.sqrt(hist14[0]),p0=p_start)\n",
"hist12_fit, _ = curve_fit(f=f, xdata= x, ydata=hist12[0], sigma=np.sqrt(hist12[0]),p0=p_start)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"hist18_fit\")\n",
"print(hist18_fit)\n",
"print(\"hist16_fit\")\n",
"print(hist16_fit)\n",
"print(\"hist14_fit\")\n",
"print(hist14_fit)\n",
"print(\"hist12_fit\")\n",
"print(hist12_fit)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"plots = [\n",
" go.Scatter(x=x,y=hist18[0], name = \"E = 18 keV\"),\n",
" go.Scatter(x=x,y=hist16[0], name = \"E = 16 keV\"),\n",
" go.Scatter(x=x,y=hist14[0], name = \"E = 14 keV\"),\n",
" go.Scatter(x=x,y=hist12[0], name = \"E = 12 keV\"),\n",
" \n",
" go.Scatter(x=x,y=f(x,*hist18_fit), name = \"E = 18 keV fit\" ),\n",
" go.Scatter(x=x,y=f(x,*hist16_fit), name = \"E = 16 keV fit\" ),\n",
" go.Scatter(x=x,y=f(x,*hist14_fit), name = \"E = 14 keV fit\" ),\n",
" go.Scatter(x=x,y=f(x,*hist12_fit), name = \"E = 12 keV fit\" )\n",
"]\n",
"\n",
"iplot(plots)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = np.asarray([12,14,16,18])\n",
"traps = np.asarray([hist12_fit[0],hist14_fit[0],hist16_fit[0],hist18_fit[0]])/(x[1]-x[0])/211673\n",
"\n",
"trap_coefs = np.polynomial.polynomial.polyfit(x = es, y = traps, deg=2)\n",
"\n",
"function_es = np.linspace(12,18)\n",
"\n",
"plots =[\n",
" go.Scatter(x=es,y=traps, name = \"Simulation\", mode = 'markers', marker = dict(size = 10)),\n",
" go.Scatter(x=function_es,y= np.polynomial.polynomial.polyval(function_es,trap_coefs), name = \"Fit\")\n",
"]\n",
"\n",
"fig = go.Figure(data = plots)\n",
"\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trap_coefs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Angular distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"angle_hist = go.Histogram(x = trap18.theta, opacity=0.5)\n",
"iplot([angle_hist])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"angle_start = go.Histogram(x = np.cos(trap18.theta_start), opacity=0.5, name = \"start\")\n",
"angle_dif = go.Histogram(x = trap18.theta_start - trap18.theta, opacity=0.5, name = \"dif\")\n",
"iplot([angle_start])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import plotly.figure_factory as ff\n",
"\n",
"\n",
"fig = ff.create_2d_density(\n",
" trap18.E, trap18.theta_start\n",
")\n",
"\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

14
notebooks/grid.kt Normal file
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@@ -0,0 +1,14 @@
arrayOf(
arrayOf(
0.0010164564160293384, 0.0007122216161639606, 0.0005970963361843862, 0.0005464969187740086, 0.0005032669119268397, 0.0004702496315294719, 0.00045034642032332564, 0.00041860573073218283, 0.0004064781180730442, 0.0003936541831559646, 0.00037810298606194944, 0.0003747374284819014, 0.0003651049705803846, 0.0003551823784047257, 0.0003534995996147017, 0.0003452017593742384, 0.00034259055090695974, 0.000338006429375515, 0.00032338366195875447, 0.00032831594461916975, 0.00032506644074877856, 0.0003185674330079961, 0.000315608063411747, 0.0003123005326865273, 0.00032442814534566593, 0.0003110239418803022, 0.0003098634047837339, 0.0003050471758329755, 0.00030139148397878535, 0.00030481506841366184, 0.00031009551220304753, 0.00031676860050831526, 0.00029976673204358973, 0.0003055113906716028, 0.00030614968607471535, 0.00030086924228532965, 0.0002966913087376838, 0.000308760894541994, 0.0003066139009133427, 0.00030957327050959183, 0.00030353847760743673, 0.00030522125639746077, 0.0003073102231712837, 0.00030632376663920064, 0.0003054533638167744, 0.0003106757807513317, 0.00030632376663920064, 0.0003055113906716028, 0.0003114881567189295, 0.00031525990228277646, 0.0003121844789768705, 0.000318973620991795, 0.0003140993651862082, 0.0003205983729269906, 0.00032605289728086155, 0.00032280339341047036, 0.0003176970301855699, 0.0003299406965543654, 0.00032413801107152387, 0.00033313217356992816, 0.0003353371940534079, 0.00033852867106897073, 0.00033243585131198717, 0.0003452017593742384, 0.00034415727598732695, 0.0003559947543723235, 0.00034613018905149304, 0.00035466013671127, 0.00035181682082467767, 0.0003631320575162185, 0.0, 0.0, 0.0, 0.0, 0.0
).toDoubleArray(),
arrayOf(
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).toDoubleArray(),
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).toDoubleArray(),
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).toDoubleArray(),
)

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2
pluto.sh
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@@ -2,5 +2,5 @@
echo "Starting notebook server"
cd notebooks && \
julia --project=. -e 'using Pkg; Pkg.instantiate(); Pkg.precompile()' && \
julia --project=. -e 'using Pkg; Pkg.instantiate();' && \
julia --project=. -e 'using Pluto; Pluto.run(require_secret_for_access=false, host="0.0.0.0")'

2
src/main/kotlin/ru/inr/mass/trapping/Main.kt
View File

@@ -33,6 +33,6 @@ fun calculate(e: Double, dir: String) {
setFields(0.2, 1.2, 7.2)
gasDensity = 1e19
initialE = e
range = 18_500 - e
range = 7500.0
}.simulateAll(1_000_000)
}