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@bricof
Created Apr 11, 2017
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Inventory Cycle with External Data

This is a modified version of the Inventory Cycle animation that uses external data instead of simulating the system in javascript. (The randomized external data for this example was produced with the accompanying events.py script.)

e1 e2 particle_id rad sil t1 t2
0 1 0 184.004633408 2 0.0 0.303804831895
0 1 4 180.757272706 1 0.00175941172025 0.233082889474
0 1 44 160.92265908 1 0.00399104062635 0.1761657534
0 1 1 203.525312959 0 0.00724791017219 0.299378675387
0 1 13 192.193142609 0 0.0077984816165 0.167242871592
0 1 58 194.431129428 0 0.00833367222139 0.236161277874
0 1 60 187.37091762 0 0.00878997055585 0.184867165362
0 1 5 188.84421435 1 0.0107648972597 0.241940914176
0 1 8 173.635132962 1 0.0110037124438 0.316892282579
0 1 14 172.056299899 1 0.0136693981678 0.187647681287
0 1 2 169.07559424 2 0.0184539079634 0.251425623707
0 1 3 206.305449664 1 0.0191064239705 0.278241300603
0 1 54 182.626262128 2 0.0197549182037 0.174144852309
0 1 149 183.236347272 2 0.0198726026625 0.207582794571
0 1 30 169.117430408 0 0.0203925247122 0.227268742372
0 1 110 169.541265405 2 0.0204880144223 0.272675533617
0 1 21 201.838280293 0 0.0277134334369 0.179300365062
0 1 126 166.41826583 2 0.027960361264 0.289340972704
0 1 88 169.333784928 2 0.0291033062513 0.283614961208
0 1 121 196.3536158 0 0.0292192060982 0.361891936829
0 1 11 164.533797699 2 0.0299340637866 0.337688358234
0 1 6 209.510635138 1 0.0316017869524 0.359168772159
0 1 7 171.25777927 1 0.037510213123 0.33404879618
0 1 15 198.779850015 2 0.0393946304011 0.328075644936
0 1 9 193.3685928 0 0.041744733678 0.221704963374
0 1 49 205.729264197 2 0.0465200379001 0.347652757954
0 1 132 208.266160437 2 0.0505710866146 0.376713610757
0 1 20 208.059246838 1 0.0548456173406 0.357010508029
0 1 74 180.440735037 2 0.0608787407954 0.377499411304
0 1 84 186.457199766 1 0.0641325006501 0.367151305375
0 1 46 179.25518265 1 0.0664449457198 0.336852956991
0 1 105 169.19913551 1 0.0714397881811 0.420047051839
0 1 31 163.909980958 0 0.0767575992961 0.385877533458
0 1 68 178.396841782 0 0.0798471543457 0.315033705182
0 1 134 202.909737111 2 0.0801468301828 0.419334806172
0 1 113 177.40506804 1 0.0923719183674 0.434986427894
0 1 10 198.039432442 1 0.095839242192 0.251442154286
0 1 19 162.267359473 2 0.0962179527066 0.391593555624
0 1 81 178.602054264 2 0.0971309546754 0.372732832046
0 1 119 181.233292068 1 0.0977336775206 0.251687384065
0 1 106 161.672859832 0 0.0980997198363 0.286057839292
0 1 26 167.03612393 2 0.0982535400504 0.254256716443
0 1 48 171.846919704 2 0.101376048336 0.377031963666
0 1 53 160.929441596 0 0.106888503901 0.351924027943
0 1 33 189.125136238 0 0.107359234417 0.316053291101
0 1 24 174.786368736 2 0.1078277743 0.36959246458
0 1 141 196.632769609 1 0.108919810958 0.430078586371
0 1 66 171.42235892 2 0.112686403943 0.350108680804
0 1 12 170.896174883 2 0.11552931062 0.312911280702
0 1 80 181.171669363 2 0.124443068554 0.459071702942
0 1 25 185.73168088 0 0.128126920409 0.337558509553
0 1 120 167.51270468 0 0.132533361334 0.382157945806
0 1 97 196.710718199 0 0.13408415373 0.483916786505
0 1 16 208.015849447 0 0.134851191992 0.354173906244
0 1 18 167.232004038 0 0.136111023045 0.418819429079
0 1 78 177.070130182 0 0.137953622057 0.353572650577
0 1 34 185.161121815 1 0.141849229423 0.458603042789
0 1 52 176.114024273 0 0.144650551821 0.34350110919
0 1 28 167.302741906 0 0.14579196552 0.495278993591
0 1 17 170.961313571 2 0.151249261597 0.376508202642
0 1 51 169.650877463 1 0.151987600302 0.318213640743
0 1 27 182.46409883 1 0.155528916457 0.380262042029
0 1 123 189.049714585 1 0.158662917514 0.497031541737
0 1 22 201.934284939 1 0.159585554287 0.481007610733
1 2 13 192.193142609 0 0.167242871592 0.402961078383
0 1 112 161.24420769 0 0.17057976924 0.47271410432
1 2 54 182.626262128 2 0.174144852309 0.477029664497
0 1 138 200.14556668 0 0.175624123517 0.327433773046
0 1 35 160.087052557 1 0.176154870639 0.490062650873
1 2 44 160.92265908 1 0.1761657534 0.357538220492
0 1 140 174.501822718 1 0.1775620066 0.353982050095
1 2 21 201.838280293 0 0.179300365062 0.487728380816
1 2 60 187.37091762 0 0.184867165362 0.395248217994
0 1 43 179.549969118 2 0.185989351042 0.360410321273
0 1 56 188.479812655 0 0.186396149269 0.414720452252
1 2 14 172.056299899 1 0.187647681287 0.359636223081
0 1 47 182.040871862 2 0.203976762305 0.544066794515
1 2 149 183.236347272 2 0.207582794571 0.474493134399
0 1 65 201.574176168 1 0.207933003487 0.444751000028
0 1 45 168.967154118 2 0.216820296372 0.48285765942
0 1 50 193.315508346 1 0.217851043595 0.429066764117
0 1 64 205.97238293 2 0.218775446253 0.524030571229
0 1 135 166.548004081 0 0.219089972965 0.505718617818
1 2 9 193.3685928 0 0.221704963374 0.452552607519
0 1 70 200.600945039 2 0.222476580304 0.550776278775
0 1 23 166.471302614 1 0.226428703968 0.385591355136
1 2 30 169.117430408 0 0.227268742372 0.481395570895
0 1 128 196.510895191 2 0.228504204607 0.481977756572
0 1 77 169.023176974 2 0.232491009414 0.540002883012
1 2 4 180.757272706 1 0.233082889474 0.540636192918
0 1 61 174.994632234 0 0.235287664333 0.515462101335
1 2 58 194.431129428 0 0.236161277874 0.569509761881
0 1 139 160.898337137 1 0.238084512814 0.392873370053
0 1 85 209.806554335 2 0.241390346551 0.522635026104
1 2 5 188.84421435 1 0.241940914176 0.53339877624
1 2 2 169.07559424 2 0.251425623707 0.591740556483
1 2 10 198.039432442 1 0.251442154286 0.543354115491
1 2 119 181.233292068 1 0.251687384065 0.483912391365
1 2 26 167.03612393 2 0.254256716443 0.41295548978
0 1 32 208.159747072 0 0.25492317391 0.604313663304
0 1 86 170.445117783 1 0.258900258135 0.60580489261
0 1 130 206.077351861 2 0.27052160183 0.422868846224
1 4 110 169.541265405 2 0.272675533617 0.47093905341
1 2 3 206.305449664 1 0.278241300603 0.518220271108
0 1 29 179.890536797 0 0.278680941332 0.546470299632
1 4 88 169.333784928 2 0.283614961208 0.553833450682
1 2 106 161.672859832 0 0.286057839292 0.444319770577
0 1 92 164.3111608 1 0.288498100688 0.612775552064
1 4 126 166.41826583 2 0.289340972704 0.625814050562
0 1 37 197.764255654 2 0.296048388491 0.504250914621
0 1 41 180.124517632 0 0.297622773611 0.63349522049
1 2 1 203.525312959 0 0.299378675387 0.625592056628
1 2 0 184.004633408 2 0.303804831895 0.589263021819
0 1 96 167.153490785 2 0.30878483839 0.547396488793
1 2 12 170.896174883 2 0.312911280702 0.607259760182
1 2 68 178.396841782 0 0.315033705182 0.621147423008
1 2 33 189.125136238 0 0.316053291101 0.654832543131
1 2 8 173.635132962 1 0.316892282579 0.485948147065
1 2 51 169.650877463 1 0.318213640743 0.617301021892
1 4 138 200.14556668 0 0.327433773046 0.663371887513
1 2 15 198.779850015 2 0.328075644936 0.589254011116
0 1 42 177.83204085 1 0.328441161383 0.624645703635
0 1 67 167.678654147 0 0.33311234519 0.674827934874
1 4 7 171.25777927 1 0.33404879618 0.636685134763
0 1 39 160.98554705 2 0.335325708412 0.618952106212
1 2 46 179.25518265 1 0.336852956991 0.651887902831
1 4 25 185.73168088 0 0.337558509553 0.522612597109
1 2 11 164.533797699 2 0.337688358234 0.529231668479
1 2 52 176.114024273 0 0.34350110919 0.494519325136
0 1 133 169.537184934 2 0.346987772934 0.598626585339
1 2 49 205.729264197 2 0.347652757954 0.541625493302
1 4 66 171.42235892 2 0.350108680804 0.684753694586
0 1 36 168.48427842 1 0.351633460441 0.6537846918
1 2 53 160.929441596 0 0.351924027943 0.621303221997
1 2 78 177.070130182 0 0.353572650577 0.669501485872
1 2 140 174.501822718 1 0.353982050095 0.568142672146
1 2 16 208.015849447 0 0.354173906244 0.619407656205
1 4 20 208.059246838 1 0.357010508029 0.645760108361
2 3 44 160.92265908 1 0.357538220492 0.548792713782
0 1 103 196.894893785 1 0.358547693377 0.631563276833
1 2 6 209.510635138 1 0.359168772159 0.605083443976
2 3 14 172.056299899 1 0.359636223081 0.523849824063
1 2 43 179.549969118 2 0.360410321273 0.569447104485
1 2 121 196.3536158 0 0.361891936829 0.654724947817
0 1 55 168.404600601 0 0.364167245085 0.57497165208
0 1 38 167.933866831 1 0.365587634715 0.613661849803
1 2 84 186.457199766 1 0.367151305375 0.59680040516
1 2 24 174.786368736 2 0.36959246458 0.573131475044
0 1 131 189.228689175 0 0.372148757389 0.570612876353
1 2 81 178.602054264 2 0.372732832046 0.615519115828
0 1 63 190.704674574 0 0.3733947022 0.62267191122
0 1 117 180.183114252 2 0.376308147361 0.705831246279
1 2 17 170.961313571 2 0.376508202642 0.679049749396
1 2 132 208.266160437 2 0.376713610757 0.574781856331
1 2 48 171.846919704 2 0.377031963666 0.63166595933
1 2 74 180.440735037 2 0.377499411304 0.65719285022
1 2 27 182.46409883 1 0.380262042029 0.723482437691
0 1 71 164.111052199 0 0.380810333036 0.689413995047
1 2 120 167.51270468 0 0.382157945806 0.625514135753
1 2 23 166.471302614 1 0.385591355136 0.536121897624
1 2 31 163.909980958 0 0.385877533458 0.546803800987
0 1 40 206.018932137 1 0.391328251749 0.552176246404
1 2 19 162.267359473 2 0.391593555624 0.605550869288
0 1 76 203.610070852 2 0.392070575567 0.67359459432
1 2 139 160.898337137 1 0.392873370053 0.59837059151
2 3 60 187.37091762 0 0.395248217994 0.579856855387
2 3 13 192.193142609 0 0.402961078383 0.709842271195
2 3 26 167.03612393 2 0.41295548978 0.570949381564
1 2 56 188.479812655 0 0.414720452252 0.738910553659
1 2 18 167.232004038 0 0.418819429079 0.61618076368
1 2 134 202.909737111 2 0.419334806172 0.692458276374
1 2 105 169.19913551 1 0.420047051839 0.747957778353
1 2 130 206.077351861 2 0.422868846224 0.718796615244
1 2 50 193.315508346 1 0.429066764117 0.645334433653
1 2 141 196.632769609 1 0.430078586371 0.679948424003
0 1 87 189.92349895 1 0.432892584806 0.631157353615
1 2 113 177.40506804 1 0.434986427894 0.612360000153
2 4 106 161.672859832 0 0.444319770577 0.716023679526
1 4 65 201.574176168 1 0.444751000028 0.620939573043
0 1 142 186.588640317 0 0.446140338158 0.664597511009
2 3 9 193.3685928 0 0.452552607519 0.75117781301
0 1 59 193.364678854 2 0.454423690845 0.62216312673
0 1 69 174.0220783 2 0.455105098785 0.625445655033
1 2 34 185.161121815 1 0.458603042789 0.768553113867
1 2 80 181.171669363 2 0.459071702942 0.795927028464
0 1 57 197.435422672 2 0.469918379647 0.672322692125
1 2 112 161.24420769 0 0.47271410432 0.766778544267
2 4 149 183.236347272 2 0.474493134399 0.634820748725
2 3 54 182.626262128 2 0.477029664497 0.756744104326
0 1 72 174.160032002 2 0.478163758407 0.682829014155
1 2 22 201.934284939 1 0.481007610733 0.657821999408
0 1 100 175.760622335 0 0.481143186385 0.761207871326
2 3 30 169.117430408 0 0.481395570895 0.7876439439
0 1 125 191.759863497 1 0.481735037569 0.725917350153
1 2 128 196.510895191 2 0.481977756572 0.691446462917
1 2 45 168.967154118 2 0.48285765942 0.673529743115
2 4 119 181.233292068 1 0.483912391365 0.745346283551
1 2 97 196.710718199 0 0.483916786505 0.638270778356
2 4 8 173.635132962 1 0.485948147065 0.786414986422
0 1 115 191.954414462 2 0.48688749938 0.755457612941
2 3 21 201.838280293 0 0.487728380816 0.837264758061
1 2 35 160.087052557 1 0.490062650873 0.748004278241
2 3 52 176.114024273 0 0.494519325136 0.833315870155
1 2 28 167.302741906 0 0.495278993591 0.815699763281
1 2 123 189.049714585 1 0.497031541737 0.789809520742
1 2 37 197.764255654 2 0.504250914621 0.663540776888
1 2 135 166.548004081 0 0.505718617818 0.855030585396
0 1 82 202.224377365 0 0.506750373107 0.797996427305
0 1 89 194.127010315 0 0.511608376351 0.720406385243
1 2 61 174.994632234 0 0.515462101335 0.729735706034
2 4 3 206.305449664 1 0.518220271108 0.789659678444
1 2 85 209.806554335 2 0.522635026104 0.749936638023
3 1 14 172.056299899 1 0.523849824063 0.862457421686
1 2 64 205.97238293 2 0.524030571229 0.764658626093
2 3 11 164.533797699 2 0.529231668479 0.85900890167
0 1 62 186.106936055 2 0.529998317327 0.791432315694
0 1 90 198.6079437 1 0.530981506569 0.749963681599
2 3 5 188.84421435 1 0.53339877624 0.81987256431
0 1 93 182.401545549 0 0.533680660331 0.813326704463
2 3 23 166.471302614 1 0.536121897624 0.687473973063
1 2 77 169.023176974 2 0.540002883012 0.84832755639
2 3 4 180.757272706 1 0.540636192918 0.701815228438
2 3 49 205.729264197 2 0.541625493302 0.761045208232
2 3 10 198.039432442 1 0.543354115491 0.800312145727
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1 2 47 182.040871862 2 0.544066794515 0.868261580878
1 2 29 179.890536797 0 0.546470299632 0.828892364825
2 3 31 163.909980958 0 0.546803800987 0.828383628103
1 2 96 167.153490785 2 0.547396488793 0.887820824449
3 4 44 160.92265908 1 0.548792713782 0.857210322923
1 2 70 200.600945039 2 0.550776278775 0.871930758357
1 2 40 206.018932137 1 0.552176246404 0.844512928794
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2 3 140 174.501822718 1 0.568142672146 0.898592562123
2 3 43 179.549969118 2 0.569447104485 0.823473155879
2 3 58 194.431129428 0 0.569509761881 0.868731476394
1 2 131 189.228689175 0 0.570612876353 0.758947659389
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2 3 24 174.786368736 2 0.573131475044 0.870148648745
2 3 132 208.266160437 2 0.574781856331 0.837609674574
1 2 55 168.404600601 0 0.57497165208 0.916811758805
3 1 60 187.37091762 0 0.579856855387 0.822413401759
2 3 15 198.779850015 2 0.589254011116 0.840526909194
2 3 0 184.004633408 2 0.589263021819 0.81542466684
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2 3 2 169.07559424 2 0.591740556483 0.798806280802
2 3 84 186.457199766 1 0.59680040516 0.924584762048
2 4 139 160.898337137 1 0.59837059151 0.919668290653
1 2 133 169.537184934 2 0.598626585339 0.785530604119
1 2 32 208.159747072 0 0.604313663304 0.902007772051
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2 3 6 209.510635138 1 0.605083443976 0.882976920658
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1 2 86 170.445117783 1 0.60580489261 0.895103043829
2 3 12 170.896174883 2 0.607259760182 0.929965726979
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1 2 92 164.3111608 1 0.612775552064 0.793673518154
1 2 38 167.933866831 1 0.613661849803 0.766092185828
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1 2 39 160.98554705 2 0.618952106212 0.920729049332
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1 2 63 190.704674574 0 0.62267191122 0.820901280083
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1 4 42 177.83204085 1 0.624645703635 0.875974026572
1 2 69 174.0220783 2 0.625445655033 0.796918517199
2 4 120 167.51270468 0 0.625514135753 0.821262945951
2 3 1 203.525312959 0 0.625592056628 0.962929198542
1 2 87 189.92349895 1 0.631157353615 0.855436657546
1 2 103 196.894893785 1 0.631563276833 0.784828218149
2 3 48 171.846919704 2 0.63166595933 0.849458491208
1 2 41 180.124517632 0 0.63349522049 0.85899833247
2 3 97 196.710718199 0 0.638270778356 0.932018043223
2 3 50 193.315508346 1 0.645334433653 0.831375606298
2 3 46 179.25518265 1 0.651887902831 0.981272141919
1 2 36 168.48427842 1 0.6537846918 0.814661825646
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2 3 121 196.3536158 0 0.654724947817 0.961932034623
2 3 33 189.125136238 0 0.654832543131 0.82290068495
2 3 74 180.440735037 2 0.65719285022 0.82775603501
2 4 22 201.934284939 1 0.657821999408 0.832960965751
2 3 37 197.764255654 2 0.663540776888 0.971559462524
1 2 142 186.588640317 0 0.664597511009 0.834457250719
2 3 78 177.070130182 0 0.669501485872 0.993479928885
1 2 57 197.435422672 2 0.672322692125 0.886780449085
2 3 45 168.967154118 2 0.673529743115 0.928567616087
1 2 76 203.610070852 2 0.67359459432 0.874287954559
1 2 67 167.678654147 0 0.674827934874 0.909078501403
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2 3 17 170.961313571 2 0.679049749396 0.896598514429
2 3 141 196.632769609 1 0.679948424003 0.893483856807
1 2 72 174.160032002 2 0.682829014155 0.854589824194
3 4 23 166.471302614 1 0.687473973063 0.950217206187
1 2 71 164.111052199 0 0.689413995047 0.942474240847
2 3 128 196.510895191 2 0.691446462917 0.905864577438
2 3 134 202.909737111 2 0.692458276374 0.982729712653
1 2 79 160.460262961 0 0.696768807598 1.03257015795
3 1 4 180.757272706 1 0.701815228438 0.884338325548
1 2 117 180.183114252 2 0.705831246279 0.881406656923
3 1 13 192.193142609 0 0.709842271195 1.01383862798
1 2 104 199.644174725 0 0.716951129138 0.999791600974
2 3 130 206.077351861 2 0.718796615244 0.997887800429
1 2 89 194.127010315 0 0.720406385243 0.90730138406
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import pandas as pd
import numpy as np
speed = 0.00015
outer_r = 210
inner_r = 160
events_data = []
for p in range(150):
rad = inner_r + (outer_r - inner_r) * np.random.uniform()
sil = int(np.random.uniform() * 3)
n_events = 2 + int(np.random.uniform() * 10)
next_time = np.random.uniform() * 0.01 * p
next_event = 0
for i in range(n_events - 1):
start_time = next_time
next_time = start_time + 0.5 * ( 0.3 + np.random.uniform() * 0.4 )
start_event = next_event
next_event = start_event + 1
if next_event > 3:
next_event = 1
events_data.append({
'particle_id': p, 'rad': rad, 'sil': sil,
't1': start_time,
't2': next_time,
'e1': start_event,
'e2': next_event
})
# add final event
events_data.append({
'particle_id': p, 'rad': rad, 'sil': sil,
't1': next_time,
't2': next_time + 0.5 * ( 0.3 + np.random.uniform() * 0.4 ),
'e1': next_event,
'e2': 4
})
df = pd.DataFrame.from_records(events_data)
df = df.sort_values(by="t1")
df.to_csv("events.csv", index=False)
<!DOCTYPE html>
<meta charset="utf-8">
<style>
.first-point-rect {
fill: #fff;
stroke: #000;
stroke-miterlimit: 10;
}
.later-points-rect {
fill: #fff;
stroke: #939598;
stroke-miterlimit: 10;
stroke-dasharray: 4.023,4.023;
}
.label {
fill: #939598;
font-size: 14px;
}
</style>
<body>
<svg width="960" height="500" viewBox="-150 0 960 665">
<g id="inventory-cycle">
</g>
<g id="point-1">
<rect class="first-point-rect" x="200" y="133.4" width="176" height="144"/>
<text class="label" transform="matrix(1 0 0 1 200.4859 297.0509)">point 1</text>
</g>
<g id="point-2">
<rect class="later-points-rect" x="392.1" y="429.2" width="38" height="144" transform="matrix(0.7071 -0.7071 0.7071 0.7071 -233.9965 437.497)"/>
<text class="label" transform="matrix(0.7 -0.7 0.7 0.7 341 458)">point 2</text>
</g>
<g id="point-3">
<rect class="later-points-rect" x="269" y="480.2" width="38" height="144"/>
<text class="label" transform="matrix(1 0 0 1 269 472.3668)">point 3</text>
</g>
</svg>
<script src="https://d3js.org/d3.v4.min.js"></script>
<script src="inventory-cycle.js"></script>
<script>
start_inventory_animation()
</script>
function form_outline_d(form_n, x, y){
// square
if (form_n == 0) {
return "M" + (x) + "," + (y) + "l10,0 l0,10 l-10,0 z"
}
// triangle
if (form_n == 1) {
return "M" + (x) + "," + (y + 10) + "l5,-10 l5,10 z"
}
// circle
if (form_n == 2) {
return "M" + (x) + "," + (y + 5) + "a 5 5 0 1 0 0.01 0 z"
}
}
function start_inventory_animation() {
// vis config variables
var svg = d3.select("#inventory-cycle")
var speed = 0.00015 // ticks per millisecond
var outer_r = 210
var inner_r = 160
var center_x = 289
var center_y_main = 380
var center_y_in = -30
// mapping functions
var coord_x_in = function(r,a) { return center_x + r * Math.cos(a) }
var coord_y_in = function(r,a) { return center_y_in+ r * Math.sin(a) }
var coord_x_main = function(r,a) { return center_x + r * Math.cos(a) }
var coord_y_main = function(r,a) { return center_y_main + r * Math.sin(a) }
var angle_in = function(t, t1, t2) {
var rel_t
if (t2 == t1) { rel_t = 1 }
else { rel_t = (t - t1) / (t2 - t1) }
var a = (180.0 - 90.0 * rel_t) * (Math.PI/180.0)
return a
}
var angle_main = function(t, t1, t2, e1, e2) {
// compute angle in circle for a dot,
// given its previous and next events, their timestamps, and the current time
var rel_t
if (t2 == t1) { rel_t = 1 }
else { rel_t = (t - t1) / (t2 - t1) }
if (e2 == 1) {
return (90.0 + 180.0 * rel_t) * (Math.PI/180.0)
}
if (e2 == 2) {
return (-90.0 + 135.0 * rel_t) * (Math.PI/180.0)
}
if (e2 == 3) {
return (-90.0 + 135.0 + 45.0 * rel_t) * (Math.PI/180.0)
}
}
d3.csv("events.csv", function(events_data){
var current_events = []
d3.timer(function(t_ms) {
var t = t_ms * speed
// *** SIMULATION ***
var current_events = []
events_data.forEach(function(d){
if ((d.t1 <= t) && (d.t2 > t)) {
current_events.push(d)
}
})
// *** UPDATE SVG ANIMATION ***
var inventory_items = svg.selectAll(".inventory-cycle-silhouette")
.data(current_events, function(d) { return d.particle_id })
inventory_items.enter().append("path")
.attr("class", "inventory-cycle-silhouette")
.attr("fill", "#fff")
.attr("stroke", "#000")
.attr("stroke-width", 0.5)
.attr("opacity", 1)
.attr("d", function(d) { return form_outline_d(d.sil, 290, 0) })
inventory_items
.attr("d", function(d){
var tx, ty
if (d.e1 == 0) {
var new_rad = inner_r + (outer_r - d.rad)
if (coord_y_in(new_rad, angle_in(t, d.t1, d.t2, d.e1, d.e2)) > center_y_main - outer_r + new_rad) {
tx = coord_x_in(new_rad, angle_in(t, d.t1, d.t2, d.e1, d.e2))
} else {
tx = -90 + d.rad * 2
}
ty = coord_y_in(new_rad, angle_in(t, d.t1, d.t2, d.e1, d.e2))
} else if (d.e2 == 4) {
tx = 280
ty = 550
} else {
tx = coord_x_main(d.rad, angle_main(t, d.t1, d.t2, d.e1, d.e2))
ty = coord_y_main(d.rad, angle_main(t, d.t1, d.t2, d.e1, d.e2))
}
return form_outline_d(d.sil, tx, ty)
})
inventory_items.exit().remove()
})
})
}
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