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authorLibravatar 20001LastOrder <boqi.chen@mail.mcgill.ca>2019-05-30 15:22:18 -0400
committerLibravatar 20001LastOrder <boqi.chen@mail.mcgill.ca>2019-05-30 15:22:18 -0400
commit44cdd1a31dff6efaad294c1ac8b7918d0be4cd8f (patch)
treef53168fb4057acdbce3cc7584c3e794217bdb9c1 /Metrics
parentfix the metric calculation could mix edge types and constraints (diff)
downloadVIATRA-Generator-44cdd1a31dff6efaad294c1ac8b7918d0be4cd8f.tar.gz
VIATRA-Generator-44cdd1a31dff6efaad294c1ac8b7918d0be4cd8f.tar.zst
VIATRA-Generator-44cdd1a31dff6efaad294c1ac8b7918d0be4cd8f.zip
measurement for controlled random generation
Diffstat (limited to 'Metrics')
-rw-r--r--Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.gitignore3
-rw-r--r--Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend10
-rw-r--r--Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend7
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb4100
4 files changed, 157 insertions, 3963 deletions
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.gitignore b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.gitignore
index b75ad9db..317e68e2 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.gitignore
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.gitignore
@@ -2,4 +2,5 @@
2bin/ 2bin/
3testInput/ 3testInput/
4testOutput/ 4testOutput/
5xtend-gen/ \ No newline at end of file 5xtend-gen/
6*.csv \ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
index 2e793b23..15df0dde 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
@@ -25,17 +25,17 @@ class Main {
25 //init model 25 //init model
26 YakindummPackageImpl.eINSTANCE.eClass; 26 YakindummPackageImpl.eINSTANCE.eClass;
27// Yakindumm2PackageImpl.eINSTANCE.eClass; 27// Yakindumm2PackageImpl.eINSTANCE.eClass;
28 val infos = initData(); 28 //val infos = initData();
29 29
30 println("Start Reading Models..."); 30 println("Start Reading Models...");
31 var reader = new GraphReader(YakindummPackageImpl.eINSTANCE); 31 var reader = new GraphReader(YakindummPackageImpl.eINSTANCE);
32 for(info : infos){ 32// for(info : infos){
33 calculateAllModels(info.inputFolder, info.outputFolder,info.numRuns, reader); 33// calculateAllModels(info.inputFolder, info.outputFolder,info.numRuns, reader);
34 } 34// }
35 35
36 //human input has different package declaration 36 //human input has different package declaration
37// reader = new GraphReader(Yakindumm2PackageImpl.eINSTANCE); 37// reader = new GraphReader(Yakindumm2PackageImpl.eINSTANCE);
38 val human = new RWInformation("inputs/humanInput/", "../plot/statistics/humanOutput/", 1); 38 val human = new RWInformation("inputs/Random/", "outputs/", 1);
39 calculateAllModels(human.inputFolder, human.outputFolder,human.numRuns, reader); 39 calculateAllModels(human.inputFolder, human.outputFolder,human.numRuns, reader);
40 40
41 41
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend
index b20ffa01..5c161f4b 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend
@@ -8,7 +8,6 @@ import org.eclipse.emf.ecore.EObject
8import org.eclipse.emf.ecore.EReference 8import org.eclipse.emf.ecore.EReference
9 9
10class EMFGraph extends Graph{ 10class EMFGraph extends Graph{
11
12 def void init (EObject root, List<Metric> metrics, String name, List<String> referenceTypes){ 11 def void init (EObject root, List<Metric> metrics, String name, List<String> referenceTypes){
13 val otherContents = root.eAllContents.toList(); 12 val otherContents = root.eAllContents.toList();
14 otherContents.add(root); 13 otherContents.add(root);
@@ -67,9 +66,15 @@ class EMFGraph extends Graph{
67 nodeInfo.add(NUM_NODE_HEADER); 66 nodeInfo.add(NUM_NODE_HEADER);
68 nodeInfo.add(this.statistic.allNodes.size()+""); 67 nodeInfo.add(this.statistic.allNodes.size()+"");
69 68
69 val stateInfo = new ArrayList<String>();
70 stateInfo.add(STATE_ID_HEADER);
71 stateInfo.add(this.name);
72
73
70 output.add(metaInfo); 74 output.add(metaInfo);
71 output.add(edgeInfo); 75 output.add(edgeInfo);
72 output.add(nodeInfo); 76 output.add(nodeInfo);
77 output.add(stateInfo);
73 } 78 }
74 79
75 def EList<EObject> getNeighbours(EObject o, EReference r){ 80 def EList<EObject> getNeighbours(EObject o, EReference r){
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
index 66189291..e5868da0 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
@@ -16,7 +16,7 @@
16 }, 16 },
17 { 17 {
18 "cell_type": "code", 18 "cell_type": "code",
19 "execution_count": 1, 19 "execution_count": 2,
20 "metadata": {}, 20 "metadata": {},
21 "outputs": [], 21 "outputs": [],
22 "source": [ 22 "source": [
@@ -48,7 +48,7 @@
48 }, 48 },
49 { 49 {
50 "cell_type": "code", 50 "cell_type": "code",
51 "execution_count": 63, 51 "execution_count": 3,
52 "metadata": {}, 52 "metadata": {},
53 "outputs": [], 53 "outputs": [],
54 "source": [ 54 "source": [
@@ -59,8 +59,7 @@
59 " self.rep = rep\n", 59 " self.rep = rep\n",
60 " self.out_d_distance, _ = stats.ks_2samp(graphStat.out_d, rep.out_d)\n", 60 " self.out_d_distance, _ = stats.ks_2samp(graphStat.out_d, rep.out_d)\n",
61 " self.na_distance,_ = stats.ks_2samp(graphStat.na, rep.na)\n", 61 " self.na_distance,_ = stats.ks_2samp(graphStat.na, rep.na)\n",
62 " self.mpc_distance,_ = stats.ks_2samp(graphStat.mpc, rep.mpc)\n", 62 " self.mpc_distance,_ = stats.ks_2samp(graphStat.mpc, rep.mpc)\n"
63 " print(self.mpc_distance)"
64 ] 63 ]
65 }, 64 },
66 { 65 {
@@ -79,7 +78,7 @@
79 }, 78 },
80 { 79 {
81 "cell_type": "code", 80 "cell_type": "code",
82 "execution_count": 3, 81 "execution_count": 4,
83 "metadata": {}, 82 "metadata": {},
84 "outputs": [], 83 "outputs": [],
85 "source": [ 84 "source": [
@@ -109,7 +108,7 @@
109 }, 108 },
110 { 109 {
111 "cell_type": "code", 110 "cell_type": "code",
112 "execution_count": 4, 111 "execution_count": 5,
113 "metadata": {}, 112 "metadata": {},
114 "outputs": [], 113 "outputs": [],
115 "source": [ 114 "source": [
@@ -140,7 +139,7 @@
140 }, 139 },
141 { 140 {
142 "cell_type": "code", 141 "cell_type": "code",
143 "execution_count": 5, 142 "execution_count": 6,
144 "metadata": {}, 143 "metadata": {},
145 "outputs": [], 144 "outputs": [],
146 "source": [ 145 "source": [
@@ -150,7 +149,7 @@
150 }, 149 },
151 { 150 {
152 "cell_type": "code", 151 "cell_type": "code",
153 "execution_count": 16, 152 "execution_count": 7,
154 "metadata": {}, 153 "metadata": {},
155 "outputs": [], 154 "outputs": [],
156 "source": [ 155 "source": [
@@ -164,7 +163,7 @@
164 }, 163 },
165 { 164 {
166 "cell_type": "code", 165 "cell_type": "code",
167 "execution_count": 21, 166 "execution_count": 8,
168 "metadata": {}, 167 "metadata": {},
169 "outputs": [], 168 "outputs": [],
170 "source": [ 169 "source": [
@@ -185,21 +184,9 @@
185 }, 184 },
186 { 185 {
187 "cell_type": "code", 186 "cell_type": "code",
188 "execution_count": 94, 187 "execution_count": 9,
189 "metadata": {}, 188 "metadata": {},
190 "outputs": [ 189 "outputs": [],
191 {
192 "name": "stdout",
193 "output_type": "stream",
194 "text": [
195 "[0.0, 0.216, 0.216, 0.45, 0.768, 0.8, 0.8, 0.8, 0.89751, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.91349, 0.91349, 0.9241, 0.9241, 0.93061, 0.93061, 0.93061, 0.93061, 0.93061, 0.93061, 0.93061, 0.93728, 0.96, 0.96, 0.975, 1.0]\n",
196 "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14933, 0.23704, 0.45, 0.45, 0.53333, 0.6, 0.6, 0.6, 0.768, 0.768, 0.768, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.85289, 0.86667, 0.88533, 0.88889, 0.88889, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.91349, 0.93061, 0.93223, 0.93728, 0.94815, 0.94815, 0.94815, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96667]\n",
197 "Ks_2sampResult(statistic=0.8904894133981257, pvalue=1.5238394791093816e-27)\n",
198 "[0.0, 0.0, 0.0, 0.0, 0.0, 0.14911, 0.64286, 0.672, 0.672, 0.7, 0.72071, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.78943, 0.84, 0.84]\n",
199 "92\n"
200 ]
201 }
202 ],
203 "source": [ 190 "source": [
204 "### Read Models\n", 191 "### Read Models\n",
205 "#read representative\n", 192 "#read representative\n",
@@ -210,14 +197,10 @@
210 "# assign rep distributions to human_rep\n", 197 "# assign rep distributions to human_rep\n",
211 "human_rep.na = human_na.na\n", 198 "human_rep.na = human_na.na\n",
212 "human_rep.mpc = human_mpc.mpc\n", 199 "human_rep.mpc = human_mpc.mpc\n",
213 "print(human_mpc.mpc)\n",
214 "# Read generated models\n", 200 "# Read generated models\n",
215 "viatra_no_con_stats = readStats('../statistics/viatraEvolve/', 1000)\n", 201 "viatra_no_con_stats = readStats('../statistics/viatra_nocon_output/', 5000)\n",
216 "print(viatra_no_con_stats[350].mpc)\n", 202 "viatra_con_stats = readStats('../statistics/viatra_con_output/',5000)\n",
217 "print(stats.ks_2samp(viatra_con_stats[300].mpc, human_mpc.mpc))\n", 203 "random_stats = readStats('../statistics/random_output/',5000)"
218 "viatra_con_stats = readStats('../statistics/viatra_con_output/',3100)\n",
219 "print(viatra_con_stats[100].mpc)\n",
220 "print(len(viatra_con_stats[30].mpc))"
221 ] 204 ]
222 }, 205 },
223 { 206 {
@@ -229,3918 +212,22 @@
229 }, 212 },
230 { 213 {
231 "cell_type": "code", 214 "cell_type": "code",
232 "execution_count": 88, 215 "execution_count": 10,
233 "metadata": {}, 216 "metadata": {},
234 "outputs": [ 217 "outputs": [],
235 {
236 "name": "stdout",
237 "output_type": "stream",
238 "text": [
239 "0.9850746268656716\n",
240 "0.9850746268656716\n",
241 "0.7805970149253731\n",
242 "0.4332285938727416\n",
243 "0.43903857336693153\n",
244 "0.41306454739290555\n",
245 "0.41905855338691156\n",
246 "0.4062380405663988\n",
247 "0.41224258454562623\n",
248 "0.4180970149253731\n",
249 "0.42380689146858297\n",
250 "0.4293775027302512\n",
251 "0.43481388239525265\n",
252 "0.6987788331071914\n",
253 "0.44012082444918266\n",
254 "0.4282160625444208\n",
255 "0.4335381913959614\n",
256 "0.4217734855136084\n",
257 "0.42710864283234984\n",
258 "0.4438154057299708\n",
259 "0.4323211528564076\n",
260 "0.4374151967435549\n",
261 "0.4423947677343619\n",
262 "0.4472636815920398\n",
263 "0.7139303482587065\n",
264 "0.4520255863539445\n",
265 "0.44103657536493357\n",
266 "0.4458144062297209\n",
267 "0.45048948804365274\n",
268 "0.45506510003175615\n",
269 "0.4595443833464258\n",
270 "0.44901806755695206\n",
271 "0.45351368159203975\n",
272 "0.4430970149253731\n",
273 "0.4326803482587065\n",
274 "0.568407960199005\n",
275 "0.4372980458532082\n",
276 "0.44182150472129145\n",
277 "0.4316174230886384\n",
278 "0.4361525704809287\n",
279 "0.4405970149253732\n",
280 "0.9850746268656716\n",
281 "0.9850746268656716\n",
282 "0.9402985074626866\n",
283 "0.9402985074626866\n",
284 "0.8805970149253731\n",
285 "0.600459242250287\n",
286 "0.8805970149253731\n",
287 "0.8805970149253731\n",
288 "0.8805970149253731\n",
289 "0.8805970149253731\n",
290 "0.8805970149253731\n",
291 "0.8805970149253731\n",
292 "0.8805970149253731\n",
293 "0.7139303482587065\n",
294 "0.7267508610792193\n",
295 "0.6498277841561424\n",
296 "0.6279317697228145\n",
297 "0.6663113006396588\n",
298 "0.6805970149253732\n",
299 "0.6930970149253731\n",
300 "0.6305970149253731\n",
301 "0.6453028972783142\n",
302 "0.6583747927031509\n",
303 "0.6583747927031509\n",
304 "0.5472636815920398\n",
305 "0.5648075412411626\n",
306 "0.4595443833464258\n",
307 "0.6139303482587064\n",
308 "0.4805970149253731\n",
309 "0.4996446339729922\n",
310 "0.5169606512890095\n",
311 "0.42605156037991854\n",
312 "0.4458144062297209\n",
313 "0.4023361453601557\n",
314 "0.42226368159203975\n",
315 "0.4405970149253732\n",
316 "0.45752009184845\n",
317 "0.4731896075179657\n",
318 "0.5472636815920398\n",
319 "0.4361525704809287\n",
320 "0.3991155334438916\n",
321 "0.4520255863539445\n",
322 "0.4668039114770972\n",
323 "0.4472636815920398\n",
324 "0.46124217621569574\n",
325 "0.3967260471834376\n",
326 "0.3644679826673086\n",
327 "0.3805970149253731\n",
328 "0.39574853007688826\n",
329 "0.5680970149253731\n",
330 "0.4100087796312555\n",
331 "0.3805970149253731\n",
332 "0.39488272921108736\n",
333 "0.36631130063965883\n",
334 "0.3805970149253731\n",
335 "0.3941105284388866\n",
336 "0.4069128043990573\n",
337 "0.41905855338691156\n",
338 "0.43059701492537317\n",
339 "0.43059701492537317\n",
340 "0.5864793678665496\n",
341 "0.4171823807790317\n",
342 "0.39279213687659265\n",
343 "0.40440653873489696\n",
344 "0.41548073585560563\n",
345 "0.42605156037991854\n",
346 "0.4361525704809287\n",
347 "0.4458144062297209\n",
348 "0.45506510003175615\n",
349 "0.4125119085423944\n",
350 "0.42226368159203975\n",
351 "0.9850746268656716\n",
352 "0.5472636815920398\n",
353 "0.4014303482587065\n",
354 "0.41120925982333234\n",
355 "0.40059701492537314\n",
356 "0.4100087796312555\n",
357 "0.41905855338691156\n",
358 "0.42776682624612783\n",
359 "0.4361525704809287\n",
360 "0.4442333785617367\n",
361 "0.4520255863539445\n",
362 "0.4595443833464258\n",
363 "0.617439120188531\n",
364 "0.4668039114770972\n",
365 "0.47381735390842394\n",
366 "0.4805970149253731\n",
367 "0.4472636815920398\n",
368 "0.43059701492537317\n",
369 "0.43797406410570094\n",
370 "0.4215806214827502\n",
371 "0.42898411169956663\n",
372 "0.4361525704809287\n",
373 "0.4430970149253731\n",
374 "0.5648075412411626\n",
375 "0.4118470149253731\n",
376 "0.41905855338691156\n",
377 "0.42605156037991854\n",
378 "0.4328358208955224\n",
379 "0.44007901668129934\n",
380 "0.4471122647631408\n",
381 "0.4539445628997868\n",
382 "0.46058440193399197\n",
383 "0.46703980099502485\n",
384 "0.47331833980781024\n",
385 "0.5805970149253731\n",
386 "0.4794271883824123\n",
387 "0.46167809600645415\n",
388 "0.4672636815920398\n",
389 "0.4727022780832678\n",
390 "0.4779996123279705\n",
391 "0.48316111748947566\n",
392 "0.4881919516342339\n",
393 "0.49309701492537317\n",
394 "0.49788096554265704\n",
395 "0.5025482344375682\n",
396 "0.5948827292110874\n",
397 "0.5071030390217587\n",
398 "0.5071030390217587\n",
399 "0.511549395877754\n",
400 "0.4996446339729922\n",
401 "0.4877398720682302\n",
402 "0.4805970149253731\n",
403 "0.4688323090430202\n",
404 "0.47362027073932667\n",
405 "0.46199236376258246\n",
406 "0.4668039114770972\n",
407 "0.6078697421981004\n",
408 "0.455309658603534\n",
409 "0.4601424694708277\n",
410 "0.4648666778467214\n",
411 "0.4536307227905416\n",
412 "0.45837479270315085\n",
413 "0.4520255863539445\n",
414 "0.45668397144711226\n",
415 "0.46124217621569574\n",
416 "0.46570339790409654\n",
417 "0.4700706991358994\n",
418 "0.6197274497079818\n",
419 "0.4743470149253731\n",
420 "0.47853515925527\n",
421 "0.48263783125190374\n",
422 "0.47243374961925066\n",
423 "0.4622296679865976\n",
424 "0.4520255863539445\n",
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4130 }
4131 ],
4132 "source": [ 218 "source": [
4133 "viatra_no_con_dic = calDistanceDic(viatra_no_con_stats, human_rep)\n", 219 "viatra_no_con_dic = calDistanceDic(viatra_no_con_stats, human_rep)\n",
4134 "viatra_con_dic = calDistanceDic(viatra_con_stats, human_rep)" 220 "viatra_con_dic = calDistanceDic(viatra_con_stats, human_rep)\n",
221 "random_dic = calDistanceDic(random_stats, human_rep)"
4135 ] 222 ]
4136 }, 223 },
4137 { 224 {
4138 "cell_type": "code", 225 "cell_type": "code",
4139 "execution_count": 66, 226 "execution_count": 11,
4140 "metadata": {}, 227 "metadata": {},
4141 "outputs": [], 228 "outputs": [],
4142 "source": [ 229 "source": [
4143 "filenames = reader.readmultiplefiles('../statistics/trajectories/', 10, False)\n", 230 "filenames = reader.readmultiplefiles('../statistics/viatra_nocon_output/trajectories/', 15, False)\n",
4144 "trajectories = {}\n", 231 "trajectories = {}\n",
4145 "for name in filenames:\n", 232 "for name in filenames:\n",
4146 " trajectories[name] = reader.readTrajectory(name)\n", 233 " trajectories[name] = reader.readTrajectory(name)\n",
@@ -4162,18 +249,18 @@
4162 }, 249 },
4163 { 250 {
4164 "cell_type": "code", 251 "cell_type": "code",
4165 "execution_count": 67, 252 "execution_count": 12,
4166 "metadata": {}, 253 "metadata": {},
4167 "outputs": [ 254 "outputs": [
4168 { 255 {
4169 "data": { 256 "data": {
4170 "application/vnd.jupyter.widget-view+json": { 257 "application/vnd.jupyter.widget-view+json": {
4171 "model_id": "75477310ae014aa797b401708d3c1388", 258 "model_id": "1bdc31418d894783b36cc79c60251f00",
4172 "version_major": 2, 259 "version_major": 2,
4173 "version_minor": 0 260 "version_minor": 0
4174 }, 261 },
4175 "text/plain": [ 262 "text/plain": [
4176 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…" 263 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
4177 ] 264 ]
4178 }, 265 },
4179 "metadata": {}, 266 "metadata": {},
@@ -4185,7 +272,7 @@
4185 "<function __main__.plot_out_degree(lines)>" 272 "<function __main__.plot_out_degree(lines)>"
4186 ] 273 ]
4187 }, 274 },
4188 "execution_count": 67, 275 "execution_count": 12,
4189 "metadata": {}, 276 "metadata": {},
4190 "output_type": "execute_result" 277 "output_type": "execute_result"
4191 } 278 }
@@ -4198,18 +285,18 @@
4198 }, 285 },
4199 { 286 {
4200 "cell_type": "code", 287 "cell_type": "code",
4201 "execution_count": 68, 288 "execution_count": 13,
4202 "metadata": {}, 289 "metadata": {},
4203 "outputs": [ 290 "outputs": [
4204 { 291 {
4205 "data": { 292 "data": {
4206 "application/vnd.jupyter.widget-view+json": { 293 "application/vnd.jupyter.widget-view+json": {
4207 "model_id": "8944902bb0c44eb2b94cfdd1f8f98332", 294 "model_id": "78565a0ec3d740908fcea753387cfc3e",
4208 "version_major": 2, 295 "version_major": 2,
4209 "version_minor": 0 296 "version_minor": 0
4210 }, 297 },
4211 "text/plain": [ 298 "text/plain": [
4212 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…" 299 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
4213 ] 300 ]
4214 }, 301 },
4215 "metadata": {}, 302 "metadata": {},
@@ -4221,7 +308,7 @@
4221 "<function __main__.plot_out_na(lines)>" 308 "<function __main__.plot_out_na(lines)>"
4222 ] 309 ]
4223 }, 310 },
4224 "execution_count": 68, 311 "execution_count": 13,
4225 "metadata": {}, 312 "metadata": {},
4226 "output_type": "execute_result" 313 "output_type": "execute_result"
4227 } 314 }
@@ -4234,18 +321,18 @@
4234 }, 321 },
4235 { 322 {
4236 "cell_type": "code", 323 "cell_type": "code",
4237 "execution_count": 69, 324 "execution_count": 14,
4238 "metadata": {}, 325 "metadata": {},
4239 "outputs": [ 326 "outputs": [
4240 { 327 {
4241 "data": { 328 "data": {
4242 "application/vnd.jupyter.widget-view+json": { 329 "application/vnd.jupyter.widget-view+json": {
4243 "model_id": "3084ff17e14447058f88cf2fb64d4595", 330 "model_id": "4392eb19fe1844c4affeb62f9ba9163b",
4244 "version_major": 2, 331 "version_major": 2,
4245 "version_minor": 0 332 "version_minor": 0
4246 }, 333 },
4247 "text/plain": [ 334 "text/plain": [
4248 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…" 335 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
4249 ] 336 ]
4250 }, 337 },
4251 "metadata": {}, 338 "metadata": {},
@@ -4257,7 +344,7 @@
4257 "<function __main__.plot_out_mpc(lines)>" 344 "<function __main__.plot_out_mpc(lines)>"
4258 ] 345 ]
4259 }, 346 },
4260 "execution_count": 69, 347 "execution_count": 14,
4261 "metadata": {}, 348 "metadata": {},
4262 "output_type": "execute_result" 349 "output_type": "execute_result"
4263 } 350 }
@@ -4270,13 +357,13 @@
4270 }, 357 },
4271 { 358 {
4272 "cell_type": "code", 359 "cell_type": "code",
4273 "execution_count": 70, 360 "execution_count": 15,
4274 "metadata": {}, 361 "metadata": {},
4275 "outputs": [ 362 "outputs": [
4276 { 363 {
4277 "data": { 364 "data": {
4278 "application/vnd.jupyter.widget-view+json": { 365 "application/vnd.jupyter.widget-view+json": {
4279 "model_id": "9c21e79436ce422ea276fa0654a8bc83", 366 "model_id": "c667f0f9dcd5494f81d95c64ad900612",
4280 "version_major": 2, 367 "version_major": 2,
4281 "version_minor": 0 368 "version_minor": 0
4282 }, 369 },
@@ -4293,7 +380,7 @@
4293 "<function __main__.plot_out_degree(lines)>" 380 "<function __main__.plot_out_degree(lines)>"
4294 ] 381 ]
4295 }, 382 },
4296 "execution_count": 70, 383 "execution_count": 15,
4297 "metadata": {}, 384 "metadata": {},
4298 "output_type": "execute_result" 385 "output_type": "execute_result"
4299 } 386 }
@@ -4306,13 +393,13 @@
4306 }, 393 },
4307 { 394 {
4308 "cell_type": "code", 395 "cell_type": "code",
4309 "execution_count": 71, 396 "execution_count": 16,
4310 "metadata": {}, 397 "metadata": {},
4311 "outputs": [ 398 "outputs": [
4312 { 399 {
4313 "data": { 400 "data": {
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4316 "version_major": 2, 403 "version_major": 2,
4317 "version_minor": 0 404 "version_minor": 0
4318 }, 405 },
@@ -4329,7 +416,7 @@
4329 "<function __main__.plot_na(lines)>" 416 "<function __main__.plot_na(lines)>"
4330 ] 417 ]
4331 }, 418 },
4332 "execution_count": 71, 419 "execution_count": 16,
4333 "metadata": {}, 420 "metadata": {},
4334 "output_type": "execute_result" 421 "output_type": "execute_result"
4335 } 422 }
@@ -4342,13 +429,13 @@
4342 }, 429 },
4343 { 430 {
4344 "cell_type": "code", 431 "cell_type": "code",
4345 "execution_count": 72, 432 "execution_count": 17,
4346 "metadata": {}, 433 "metadata": {},
4347 "outputs": [ 434 "outputs": [
4348 { 435 {
4349 "data": { 436 "data": {
4350 "application/vnd.jupyter.widget-view+json": { 437 "application/vnd.jupyter.widget-view+json": {
4351 "model_id": "88a4e2356bdd45a4a51c9c5b71b8896b", 438 "model_id": "be65f39c2fae4c84a1d6908f3b70a86e",
4352 "version_major": 2, 439 "version_major": 2,
4353 "version_minor": 0 440 "version_minor": 0
4354 }, 441 },
@@ -4365,7 +452,7 @@
4365 "<function __main__.plot_mpc(lines)>" 452 "<function __main__.plot_mpc(lines)>"
4366 ] 453 ]
4367 }, 454 },
4368 "execution_count": 72, 455 "execution_count": 17,
4369 "metadata": {}, 456 "metadata": {},
4370 "output_type": "execute_result" 457 "output_type": "execute_result"
4371 } 458 }
@@ -4378,10 +465,111 @@
4378 }, 465 },
4379 { 466 {
4380 "cell_type": "code", 467 "cell_type": "code",
4381 "execution_count": null, 468 "execution_count": 18,
4382 "metadata": {}, 469 "metadata": {},
4383 "outputs": [], 470 "outputs": [
4384 "source": [] 471 {
472 "data": {
473 "application/vnd.jupyter.widget-view+json": {
474 "model_id": "1de0db5b5c8d46de958f6b43144dac54",
475 "version_major": 2,
476 "version_minor": 0
477 },
478 "text/plain": [
479 "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
480 ]
481 },
482 "metadata": {},
483 "output_type": "display_data"
484 },
485 {
486 "data": {
487 "text/plain": [
488 "<function __main__.plot_out_degree(lines)>"
489 ]
490 },
491 "execution_count": 18,
492 "metadata": {},
493 "output_type": "execute_result"
494 }
495 ],
496 "source": [
497 "def plot_out_degree(lines):\n",
498 " plot(random_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
499 "interact(plot_out_degree, lines=[[]])"
500 ]
501 },
502 {
503 "cell_type": "code",
504 "execution_count": 20,
505 "metadata": {},
506 "outputs": [
507 {
508 "data": {
509 "application/vnd.jupyter.widget-view+json": {
510 "model_id": "6f8fa855125b4beca603abbf801412ac",
511 "version_major": 2,
512 "version_minor": 0
513 },
514 "text/plain": [
515 "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
516 ]
517 },
518 "metadata": {},
519 "output_type": "display_data"
520 },
521 {
522 "data": {
523 "text/plain": [
524 "<function __main__.plot_out_degree(lines)>"
525 ]
526 },
527 "execution_count": 20,
528 "metadata": {},
529 "output_type": "execute_result"
530 }
531 ],
532 "source": [
533 "def plot_out_degree(lines):\n",
534 " plot(random_dic, lines, 0, lambda a: a.na_distance, colors, 'out degree')\n",
535 "interact(plot_out_degree, lines=[[]])"
536 ]
537 },
538 {
539 "cell_type": "code",
540 "execution_count": 23,
541 "metadata": {},
542 "outputs": [
543 {
544 "data": {
545 "application/vnd.jupyter.widget-view+json": {
546 "model_id": "b4ed2adb29004908a3799bc91bf0662b",
547 "version_major": 2,
548 "version_minor": 0
549 },
550 "text/plain": [
551 "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
552 ]
553 },
554 "metadata": {},
555 "output_type": "display_data"
556 },
557 {
558 "data": {
559 "text/plain": [
560 "<function __main__.plot_out_degree(lines)>"
561 ]
562 },
563 "execution_count": 23,
564 "metadata": {},
565 "output_type": "execute_result"
566 }
567 ],
568 "source": [
569 "def plot_out_degree(lines):\n",
570 " plot(random_dic, lines, 0, lambda a: a.mpc_distance, colors, 'out degree')\n",
571 "interact(plot_out_degree, lines=[[]])"
572 ]
4385 }, 573 },
4386 { 574 {
4387 "cell_type": "code", 575 "cell_type": "code",
generated by cgit v1.2.3-54-g00ecf (git 2.45.2) at 2024-06-26 17:40:02 +0000