2018-04-08 22:03:23 +03:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-04-08T18:49+0000",
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"start_time": "2018-04-08T18:49+0000"
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}
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},
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"outputs": [
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{
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2018-12-03 12:23:27 +03:00
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"data": {
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"application/vnd.jupyter.widget-view+json": {
|
2018-12-17 20:47:17 +03:00
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"model_id": "b3666d93-1a9f-47f6-b272-8c9cde5eb9eb",
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2018-12-03 12:23:27 +03:00
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"version_major": 2,
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"version_minor": 0
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
|
2018-12-17 20:47:17 +03:00
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"model_id": "0e65aaab-812e-45f3-98df-43ed93255c5e",
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2018-12-03 12:23:27 +03:00
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"version_major": 2,
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"version_minor": 0
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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2018-04-08 22:03:23 +03:00
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}
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],
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"source": [
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"%classpath add jar ../build/install/trapping/lib/commons-math3-3.6.1.jar\n",
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"%classpath add jar ../build/install/trapping/lib/trapping-dev.jar"
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]
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},
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{
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"cell_type": "code",
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2018-12-17 20:47:17 +03:00
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"execution_count": 5,
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2018-04-08 22:03:23 +03:00
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-04-08T18:58+0000",
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"start_time": "2018-04-08T18:58+0000"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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2018-12-17 20:47:17 +03:00
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"model_id": "8e62373b-9e28-4669-937f-06f5c908a4bd",
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2018-04-08 22:03:23 +03:00
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"version_major": 2,
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"version_minor": 0
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
2018-12-03 12:23:27 +03:00
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"scatter = inr.numass.trapping.Scatter.INSTANCE\n",
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2018-04-08 22:03:23 +03:00
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"\n",
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"def e = (1..20) // energy in keV\n",
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"\n",
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2018-12-03 12:23:27 +03:00
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"factor = 1e-22\n",
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2018-12-17 20:47:17 +03:00
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"sigmaEl = e.collect{scatter.sigmael(it*1000)/factor}\n",
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"sigmaIon = e.collect{scatter.sigmaion(it*1000)/factor}\n",
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"sigmaExc = e.collect{scatter.sigmaexc(it*1000)/factor}\n",
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2018-04-08 22:03:23 +03:00
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"\n",
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"def plot = new Plot(logY: true, yLogBase: 10)\n",
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"plot << new Line(x: e, y: sigmaEl, displayName: \"Elastic\")\n",
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"plot << new Line(x: e, y: sigmaIon, displayName: \"Ionization\")\n",
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"plot << new Line(x: e, y: sigmaExc, displayName: \"Excitation\")\n"
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]
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},
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2018-12-03 12:23:27 +03:00
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{
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"cell_type": "code",
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2018-12-17 20:47:17 +03:00
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"execution_count": 4,
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2018-12-03 12:23:27 +03:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"E(keV)\telastic\tion\texc\n",
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"12.0\t0.43948540252301355\t2.7362696996880764\t2.2754271467658227\n",
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"12.1\t0.4360219815420227\t2.716347129996631\t2.259247553470829\n",
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"12.2\t0.4326153394877464\t2.6967291910800784\t2.2433117665045716\n",
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"12.3\t0.42926409223681455\t2.6774088086825643\t2.227614186457129\n",
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"12.4\t0.42596690029168804\t2.6583791281775273\t2.212149386181278\n",
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"12.5\t0.4227224669965498\t2.639633506055748\t2.1969121041685504\n",
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"12.6\t0.41952953683810995\t2.621165501807934\t2.1818972382301722\n",
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"12.7\t0.4163868938266485\t2.602968870180603\t2.1670998394665926\n",
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"12.8\t0.4132933599529065\t2.585037553785318\t2.1525151065102723\n",
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"12.9\t0.4102477937167139\t2.567365676042546\t2.138138380027344\n",
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"13.0\t0.4072490887234942\t2.5499475344425586\t2.123965137464613\n",
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"13.1\t0.4042961723450181\t2.532777594106803\t2.1099909880291814\n",
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"13.2\t0.40138800444100226\t2.515850481634202\t2.096211667888705\n",
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"13.3\t0.3985235761383543\t2.4991609792177183\t2.082623035581028\n",
|
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|
|
"13.4\t0.3957019086650494\t2.4827040190174023\t2.069221067622563\n",
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|
"13.5\t0.39292205223581\t2.466474677776929\t2.0560018543054204\n",
|
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|
|
"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",
|
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|
|
"14.0\t0.3796185919582249\t2.3885794317439313\t1.9925177389113418\n",
|
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|
|
"14.1\t0.3770711510289289\t2.373619890173301\t1.9803188348317768\n",
|
|
|
|
"14.2\t0.3745596003888268\t2.358857146684646\t1.9682781856327505\n",
|
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|
|
"14.3\t0.3720831874354002\t2.3442872663683785\t1.9563926602783916\n",
|
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|
"14.4\t0.36964118046236066\t2.32990641959609\t1.9446592107822769\n",
|
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|
|
"14.5\t0.36723286793941123\t2.315710878500726\t1.9330748694515214\n",
|
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|
|
"14.6\t0.3648575578215951\t2.3016970135976464\t1.9216367462404509\n",
|
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|
"14.7\t0.36251457688682004\t2.287861290540006\t1.910342026208781\n",
|
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|
"14.8\t0.3602032701002296\t2.274200267002247\t1.8991879670794969\n",
|
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|
"14.9\t0.357923000004158\t2.26071058968582\t1.8881718968918926\n",
|
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|
"15.0\t0.3556731461324769\t2.2473889914415754\t1.8772912117454528\n",
|
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|
"15.1\t0.353453104448203\t2.234232288503513\t1.8665433736304802\n",
|
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|
|
"15.2\t0.35126228680329497\t2.221237377828907\t1.8559259083415982\n",
|
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|
|
"15.3\t0.3491001204196282\t2.2084012345400286\t1.845436403470438\n",
|
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|
"15.4\t0.3469660473901797\t2.1957209094629615\t1.835072506474014\n",
|
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|
"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",
|
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|
|
"16.2\t0.33084241915698676\t2.0995570491305764\t1.7564192535307461\n",
|
|
|
|
"16.3\t0.32893831074590485\t2.0881577607709536\t1.7470889725889123\n",
|
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|
|
"16.4\t0.3270574375479805\t2.0768884825827687\t1.7378636896680617\n",
|
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|
|
"16.5\t0.3251993772703896\t2.0657469595720115\t1.7287416018862318\n",
|
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|
|
"16.6\t0.32336371779207285\t2.054730989121034\t1.7197209478913829\n",
|
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|
|
"16.7\t0.3215500568593091\t2.043838419467228\t1.7108000066636726\n",
|
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|
|
"16.8\t0.31975800179215863\t2.0330671482346325\t1.7019770963591483\n",
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|
"16.9\t0.3179871692013229\t2.022415121016355\t1.693250573193194\n",
|
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|
"17.0\t0.3162371847149949\t2.011880330005736\t1.684618830362136\n",
|
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|
"17.1\t0.3145076827152896\t2.0014608126743285\t1.6760802970014965\n",
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|
"17.2\t0.31279830608386505\t1.991154650494809\t1.6676334371794341\n",
|
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|
"17.3\t0.31110870595636253\t1.9809599677070577\t1.6592767489239986\n",
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"17.4\t0.3094385414853119\t1.9708749301257074\t1.6510087632828718\n",
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"17.5\t0.3077874796111622\t1.9608977439875288\t1.6428280434143254\n",
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|
"17.6\t0.30615519484111486\t1.9510266548371218\t1.6347331837082066\n",
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"17.7\t0.3045413690354524\t1.9412599464494087\t1.6267228089357717\n",
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"17.8\t0.3029456912010658\t1.9315959397875393\t1.6187955734272899\n",
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"17.9\t0.3013678572919011\t1.9220329919948316\t1.6109501602763423\n",
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"18.0\t0.29980757001605496\t1.9125694954194674\t1.603185280569817\n",
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"18.1\t0.29826453864926233\t1.9032038766707016\t1.5954996726426307\n",
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"18.2\t0.29673847885453164\t1.8939345957053948\t1.5878921013562524\n",
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"18.3\t0.29522911250768985\t1.8847601449437406\t1.5803613574001418\n",
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"18.4\t0.2937361675286164\t1.8756790484131034\t1.5729062566152603\n",
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"18.5\t0.2922593777179467\t1.866689860918917\t1.565525639338836\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"null"
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]
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},
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2018-12-17 20:47:17 +03:00
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"execution_count": 4,
|
2018-12-03 12:23:27 +03:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"println \"E(keV)\\telastic\\tion\\texc\"\n",
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"for(d = 12.0; d < 18.6; d += 0.1){\n",
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" def sigmaEl = scatter.sigmael(d*1000)/factor\n",
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" def sigmaIon = scatter.sigmaion(d*1000)/factor\n",
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" def sigmaExc = scatter.sigmaexc(d*1000)/factor\n",
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" println \"$d\\t$sigmaEl\\t$sigmaIon\\t$sigmaExc\"\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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2018-12-17 20:47:17 +03:00
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"execution_count": 21,
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2018-12-03 12:23:27 +03:00
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"metadata": {},
|
2018-12-17 20:47:17 +03:00
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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|
"model_id": "b29b147e-ab0d-473b-b9db-909013996ad7",
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"version_major": 2,
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"version_minor": 0
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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|
"import com.twosigma.beakerx.table.*\n",
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|
"\n",
|
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|
|
"def list = []\n",
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|
" \n",
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|
|
"for(d = 12.0; d < 18.6; d += 0.1){\n",
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|
" list << [\n",
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|
" \"x\": d,\n",
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|
" \"el\": scatter.sigmael(d*1000)/factor,\n",
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|
|
" \"ion\": scatter.sigmaion(d*1000)/factor,\n",
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|
|
" \"exc\": scatter.sigmaexc(d*1000)/factor \n",
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|
" ]\n",
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|
"}\n",
|
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|
|
"beakerx[\"table\"] = new TableDisplay(list)\n",
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|
|
"list"
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|
|
]
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|
},
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{
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|
"cell_type": "code",
|
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|
|
"execution_count": 23,
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|
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|
"metadata": {},
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|
"outputs": [
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|
|
{
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|
"data": {
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|
|
"text/plain": [
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|
|
"[{'x': 12.0,\n",
|
|
|
|
" 'el': 0.43948540252301355,\n",
|
|
|
|
" 'ion': 2.7362696996880764,\n",
|
|
|
|
" 'exc': 2.2754271467658227},\n",
|
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|
|
" {'x': 12.1,\n",
|
|
|
|
" 'el': 0.4360219815420227,\n",
|
|
|
|
" 'ion': 2.716347129996631,\n",
|
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|
|
" 'exc': 2.259247553470829},\n",
|
|
|
|
" {'x': 12.2,\n",
|
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|
|
" '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",
|
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|
|
" {'x': 12.7,\n",
|
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|
|
" 'el': 0.4163868938266485,\n",
|
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|
|
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" 'exc': 1.565525639338836}]"
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]
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},
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|
"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
|
|
|
"%%python\n",
|
|
|
|
"from beakerx.object import beakerx\n",
|
|
|
|
"import pandas as pd\n",
|
|
|
|
"beakerx.table"
|
|
|
|
]
|
2018-12-03 12:23:27 +03:00
|
|
|
},
|
2018-04-08 22:03:23 +03:00
|
|
|
{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Groovy",
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"language": "groovy",
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"name": "groovy"
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},
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"language_info": {
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"codemirror_mode": "groovy",
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"file_extension": ".groovy",
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"mimetype": "",
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"name": "Groovy",
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"nbconverter_exporter": "",
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"version": "2.4.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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