Differences between revisions 5 and 31 (spanning 26 versions)
Revision 5 as of 2010-01-06 22:08:41
Size: 2034
Editor: SteveLudtke
Comment:
Revision 31 as of 2016-06-17 12:36:48
Size: 3452
Editor: SteveLudtke
Comment:
Deletions are marked like this. Additions are marked like this.
Line 1: Line 1:
== Particle Box Size and Speed ==
Various algorithms in EMAN2 will depend non-linearly on the box size of the particle. Sometimes (such as the case with FFTs), this behavior will appear bizzare. For example refinements with a box size of 45 pixels will run roughly twice as fast as those with a box size of 47, and 44 is about 20% faster than 45.
== Particle Box Size ==
''Warning:'' For single particle analysis '''the particle box-size must be 1.5-2x the size of the largest axis of your particle'''. The size should also be selected from the list below. There are several important reasons for this, including proper CTF correction, good centering, and other issues. If you are stuck with data which was boxed with an insufficient size, the --extrapad option in e2ctf.py will help mitigate the problem, but will still not produce results as good as data that was properly boxed with sufficient padding.
Line 4: Line 4:
The following plot shows how long it takes to compute one similarity matrix element for a noisy particle aligned to a noise-free reference with the rotate-translate-flip aligner, refine alignment enabled with the dot comparator, and a phase residual for a similarity metric. ie - typical options for a real refinement: ''Reminder:'' The appropriate sampling for images for single particle reconstruction is ~2/3 Nyquist. That is, take the best resolution you hope to achieve, and divide by 3. This is close to the optimal A/pix value for your project. If your sampling is worse than this (A/pix larger), then you are not using a high enough magnification on the microscope. If your data is significantly oversampled (smaller A/pix than needed), e2ctf_auto will automatically generate downsampled versions of your data for more efficient processing.
----
''For those who don't like to read (a detailed discussion is below), here is the list of good box sizes:'' :

 * updated on 6/17/2016:
'''32, 36, 40, 48, 52, 56, 64, 66, 70, 72, 80, 84, 88, 100, 104, 108, 112, 120, 128, 130, 132, 140, 144, 150, 160, 162, 168, 176, 180, 182, 192, 200, 208, 216, 220, 224, 240, 256, 264, 288, 300, 308, 320, 324, 336, 338, 352, 364, 384, 400, 420, 432, 448, 450, 462, 480, 486, 500, 504, 512, 520, 528, 546, 560, 576, 588, 600, 640, 648, 650, 660, 672, 686, 700, 702, 704, 720, 726, 728, 750, 768, 770, 784, 800, 810, 840, 882, 896, 910, 924, 936, 972, 980, 1008, 1014, 1020, 1024'''

for traditional single particle analysis. If you were to pick a size not on this list, moving up to the next number on the list would make your refinement FASTER, sometimes MUCH faster. For example, refinements would take almost 2x longer with a box size of 134 as compared to 136

These sizes are less well tested, but also probably good:
1080, 1125, 1152, 1200, 1215, 1250, 1280, 1296, 1350, 1440, 1458, 1500, 1536, 1600, 1620, 1728, 1800, 1875, 1920, 1944, 2000, 2025, 2048, 2160, 2187, 2250, 2304, 2400, 2430, 2500, 2560, 2592, 2700, 2880, 2916, 3000, 3072, 3125, 3200, 3240, 3375, 3456, 3600, 3645, 3750, 3840, 3888, 4000, 4050, 4320, 4374, 4500, 4608, 4800, 4860, 5000, 5120, 5184, 5400, 5625, 5760, 5832, 6000, 6075, 6144, 6250, 6400, 6480, 6750, 6912, 7200, 7290, 7500, 7680, 7776, 8000, 8100,
----

Various algorithms in EMAN2 will depend non-linearly on the box size of the particle. Sometimes (such as the case with FFTs), this behavior will appear bizzare. For example refinements with a box size of 128 will run almost 2x faster than a box size of 122.

For several important reasons including accurate CTF correction and proper centering, box sizes in EMAN must be 1.5-2x larger than the longest axis of your particle. Sometimes for large viruses, this is reduced to 1.25x due to the very large box sizes involved, but the chance of artifacts at the edge of the box will be increased.

The following plot is a section of the timing tests used to produce the above list (only even numbers shown). This is the time required for a typical set of operations used in 3-D refinement at each size:
Line 8: Line 25:
Clearly there are some good box sizes, and some very bad box sizes. The complete timing table is also available:
Line 10: Line 27:
A better way to plot this is with respect to anticipated speed for an N^2 algorithm. This is the reciprocal of the same plot divided by box size squared,
normalized so 512 is 1. That is, larger values indicate better relative speeds. Of course, 103 is still faster than 512, but if you look in a local neighborhood
for a peak, that will correspond to a good box size to use.

{{attachment:rel_speed.jpg}}

Of course, that plot is very difficult to read actual values off of. The original timing data can be downloaded as [[attachment:profile.txt]]

From this plot, we can compute when using a larger box-size is better. ie - if you have a box size of 482, your refinement would actually run faster with a box
size of 512, even though it's larger. So, when picking a box size, you can optimize your speed by rounding up to a value from this list :

32, 33, 36, 40, 42, 44, 48, 50, 52, 54, 56, 60, 64, 66, 70, 72, 81, 84, 96, 98, 100, 104, 105, 112, 120, 128, 130, 132, 140, 150, 154, 168, 180, 182, 192, 196, 208, 210, 220, 224, 240, 250, 256, 260, 288, 300, 330, 352, 360, 384, 416, 440, 448, 450, 480, 512

Also note that if you are using shrink= it's a good idea to also confirm that your box size divided by the shrink value is in this list.
{{attachment:time_vs_size.txt}}

Particle Box Size

Warning: For single particle analysis the particle box-size must be 1.5-2x the size of the largest axis of your particle. The size should also be selected from the list below. There are several important reasons for this, including proper CTF correction, good centering, and other issues. If you are stuck with data which was boxed with an insufficient size, the --extrapad option in e2ctf.py will help mitigate the problem, but will still not produce results as good as data that was properly boxed with sufficient padding.

Reminder: The appropriate sampling for images for single particle reconstruction is ~2/3 Nyquist. That is, take the best resolution you hope to achieve, and divide by 3. This is close to the optimal A/pix value for your project. If your sampling is worse than this (A/pix larger), then you are not using a high enough magnification on the microscope. If your data is significantly oversampled (smaller A/pix than needed), e2ctf_auto will automatically generate downsampled versions of your data for more efficient processing.


For those who don't like to read (a detailed discussion is below), here is the list of good box sizes: :

  • updated on 6/17/2016:

32, 36, 40, 48, 52, 56, 64, 66, 70, 72, 80, 84, 88, 100, 104, 108, 112, 120, 128, 130, 132, 140, 144, 150, 160, 162, 168, 176, 180, 182, 192, 200, 208, 216, 220, 224, 240, 256, 264, 288, 300, 308, 320, 324, 336, 338, 352, 364, 384, 400, 420, 432, 448, 450, 462, 480, 486, 500, 504, 512, 520, 528, 546, 560, 576, 588, 600, 640, 648, 650, 660, 672, 686, 700, 702, 704, 720, 726, 728, 750, 768, 770, 784, 800, 810, 840, 882, 896, 910, 924, 936, 972, 980, 1008, 1014, 1020, 1024

for traditional single particle analysis. If you were to pick a size not on this list, moving up to the next number on the list would make your refinement FASTER, sometimes MUCH faster. For example, refinements would take almost 2x longer with a box size of 134 as compared to 136

These sizes are less well tested, but also probably good: 1080, 1125, 1152, 1200, 1215, 1250, 1280, 1296, 1350, 1440, 1458, 1500, 1536, 1600, 1620, 1728, 1800, 1875, 1920, 1944, 2000, 2025, 2048, 2160, 2187, 2250, 2304, 2400, 2430, 2500, 2560, 2592, 2700, 2880, 2916, 3000, 3072, 3125, 3200, 3240, 3375, 3456, 3600, 3645, 3750, 3840, 3888, 4000, 4050, 4320, 4374, 4500, 4608, 4800, 4860, 5000, 5120, 5184, 5400, 5625, 5760, 5832, 6000, 6075, 6144, 6250, 6400, 6480, 6750, 6912, 7200, 7290, 7500, 7680, 7776, 8000, 8100,


Various algorithms in EMAN2 will depend non-linearly on the box size of the particle. Sometimes (such as the case with FFTs), this behavior will appear bizzare. For example refinements with a box size of 128 will run almost 2x faster than a box size of 122.

For several important reasons including accurate CTF correction and proper centering, box sizes in EMAN must be 1.5-2x larger than the longest axis of your particle. Sometimes for large viruses, this is reduced to 1.25x due to the very large box sizes involved, but the chance of artifacts at the edge of the box will be increased.

The following plot is a section of the timing tests used to produce the above list (only even numbers shown). This is the time required for a typical set of operations used in 3-D refinement at each size:

rel_time.jpg

The complete timing table is also available:

   1 32	0.988
   2 33	1.069
   3 34	1.106
   4 35	1.094
   5 36	1.165
   6 37	1.424
   7 38	1.265
   8 39	1.179
   9 40	1.276
  10 41	1.600
  11 42	1.343
  12 43	1.535
  13 44	1.332
  14 45	1.320
  15 46	1.494
  16 47	1.801
  17 48	1.377
  18 49	1.419
  19 50	1.615
  20 51	1.705
  21 52	1.554
  22 53	2.138
  23 54	1.690
  24 55	1.638
  25 56	1.718
  26 57	1.879
  27 58	2.003
  28 59	2.365
  29 60	2.006
  30 61	2.862
  31 62	2.210
  32 63	1.896
  33 64	1.786
  34 65	2.006
  35 66	2.079
  36 67	3.201
  37 68	2.474
  38 69	2.545
  39 70	2.287
  40 71	3.430
  41 72	2.432
  42 73	3.909
  43 74	4.244
  44 75	2.471
  45 76	3.115
  46 77	2.531
  47 78	2.632
  48 79	4.350
  49 80	2.551
  50 81	2.667
  51 82	4.882
  52 83	4.789
  53 84	2.807
  54 85	3.225
  55 86	3.853
  56 87	3.695
  57 88	3.092
  58 89	5.365
  59 90	3.250
  60 91	3.182
  61 92	3.990
  62 93	4.177
  63 94	5.320
  64 95	3.984
  65 96	3.698
  66 97	6.917
  67 98	3.580
  68 99	3.776
  69 100	3.479
  70 101	7.398
  71 102	4.518
  72 103	7.277
  73 104	4.011
  74 105	4.163
  75 106	6.586
  76 107	7.681
  77 108	4.165
  78 109	8.548
  79 110	4.390
  80 111	7.136
  81 112	4.195
  82 113	9.037
  83 114	5.347
  84 115	5.656
  85 116	6.363
  86 117	4.773
  87 118	7.127
  88 119	5.843
  89 120	4.996
  90 121	5.381
  91 122	9.775
  92 123	8.919
  93 124	6.940
  94 125	5.361
  95 126	5.498
  96 127	10.793
  97 128	5.262
  98 129	8.172
  99 130	5.772
 100 131	13.207
 101 132	6.145
 102 133	7.136
 103 134	11.406
 104 135	6.177
 105 136	7.235
 106 137	13.460
 107 138	7.750
 108 139	13.445
 109 140	6.413
 110 141	10.361
 111 142	10.080
 112 143	7.249
 113 144	6.479
 114 145	9.548
 115 146	14.477
 116 147	7.912
 117 148	15.151
 118 149	15.361
 119 150	7.076
 120 151	17.844
 121 152	8.839
 122 153	9.007
 123 154	7.642
 124 155	10.283
 125 156	7.919
 126 157	17.080
 127 158	12.018
 128 159	13.628
 129 160	7.590
 130 161	10.389
 131 162	8.826
 132 163	21.548
 133 164	17.547
 134 165	9.322
 135 166	16.076
 136 167	21.025
 137 168	9.276
 138 169	9.698
 139 170	11.327
 140 171	11.459
 141 172	13.966
 142 173	30.264
 143 174	12.970
 144 175	9.918
 145 176	9.464
 146 177	15.912
 147 178	17.130
 148 179	29.904
 149 180	9.749
 150 181	27.213
 151 182	10.466
 152 183	19.676
 153 184	13.238
 154 185	19.103
 155 186	14.855
 156 187	13.501
 157 188	19.260
 158 189	12.914
 159 190	14.059
 160 191	29.577
 161 192	10.762
 162 193	30.683
 163 194	22.662
 164 195	12.734
 165 196	11.653
 166 197	28.955
 167 198	12.531
 168 199	30.151
 169 200	11.476
 170 201	24.117
 171 202	25.374
 172 203	17.047
 173 204	14.878
 174 205	23.362
 175 206	23.263
 176 207	16.795
 177 208	13.418
 178 209	17.106
 179 210	14.108
 180 211	33.806
 181 212	24.339
 182 213	25.485
 183 214	24.089
 184 215	21.590
 185 216	14.002
 186 217	19.506
 187 218	28.885
 188 219	30.216
 189 220	14.976
 190 221	20.136
 191 222	31.080
 192 223	46.027
 193 224	15.144
 194 225	16.064
 195 226	28.599
 196 227	46.237
 197 228	18.853
 198 229	41.577
 199 230	20.160
 200 231	17.638
 201 232	21.303
 202 233	45.457
 203 234	17.581
 204 235	27.343
 205 236	25.852
 206 237	32.117
 207 238	20.241
 208 239	46.253
 209 240	16.172
 210 241	46.068
 211 242	18.771
 212 243	19.281
 213 244	37.050
 214 245	19.471
 215 246	37.249
 216 247	23.412
 217 248	24.636
 218 249	37.858
 219 250	19.071
 220 251	51.429
 221 252	18.430
 222 253	25.778
 223 254	28.485
 224 255	24.604
 225 256	18.041
 226 257	50.685
 227 258	29.540
 228 259	36.156
 229 260	20.860
 230 261	28.165
 231 262	41.947
 232 263	61.513
 233 264	20.543
 234 265	37.414
 235 266	25.715
 236 267	44.078
 237 268	42.174
 238 269	63.978
 239 270	23.082
 240 271	60.436
 241 272	27.103
 242 273	24.942
 243 274	36.982
 244 275	25.017
 245 276	28.259
 246 277	61.920
 247 278	38.480
 248 279	32.352
 249 280	23.059
 250 281	57.747
 251 282	40.768
 252 283	68.900
 253 284	38.690
 254 285	31.115
 255 286	25.449
 256 287	44.558
 257 288	23.006
 258 289	36.072
 259 290	35.098
 260 291	57.207
 261 292	52.831
 262 293	77.769
 263 294	26.132
 264 295	43.840
 265 296	57.136
 266 297	31.309
 267 298	39.686
 268 299	36.080
 269 300	24.802
 270 301	42.340
 271 302	56.261
 272 303	62.274
 273 304	32.674
 274 305	57.124
 275 306	35.903
 276 307	84.012
 277 308	27.843
 278 309	67.110
 279 310	38.245
 280 311	86.697
 281 312	30.523
 282 313	84.909
 283 314	42.817
 284 315	32.813
 285 316	50.789
 286 317	79.510
 287 318	67.386
 288 319	43.672
 289 320	28.212
 290 321	66.876
 291 322	39.774
 292 323	46.617
 293 324	31.656
 294 325	36.138
 295 326	67.898
 296 327	73.863
 297 328	66.484
 298 329	55.413
 299 330	34.495
 300 331	87.178
 301 332	60.054
 302 333	66.211
 303 334	57.758
 304 335	68.357
 305 336	33.411
 306 337	91.447
 307 338	36.933
 308 339	77.641
 309 340	42.280
 310 341	50.504
 311 342	42.828
 312 343	38.739
 313 344	54.348
 314 345	47.889
 315 346	60.307
 316 347	105.448
 317 348	48.453
 318 349	101.230
 319 350	39.074
 320 351	41.096
 321 352	37.043
 322 353	92.030
 323 354	58.770
 324 355	70.667
 325 356	67.151
 326 357	48.228
 327 358	62.838
 328 359	111.903
 329 360	40.466
 330 361	57.183
 331 362	78.888
 332 363	45.242
 333 364	39.407
 334 365	83.464
 335 366	83.931
 336 367	123.511
 337 368	50.523
 338 369	82.153
 339 370	85.348
 340 371	72.424
 341 372	55.863
 342 373	117.396
 343 374	51.904
 344 375	46.613
 345 376	72.811
 346 377	60.640
 347 378	44.486
 348 379	105.228
 349 380	53.382
 350 381	92.783
 351 382	63.294
 352 383	123.405
 353 384	40.457
 354 385	49.395
 355 386	93.434
 356 387	69.715
 357 388	91.087
 358 389	127.701
 359 390	50.157
 360 391	75.241
 361 392	44.931
 362 393	118.510
 363 394	69.480
 364 395	88.236
 365 396	46.810
 366 397	118.600
 367 398	68.979
 368 399	62.579
 369 400	44.363
 370 401	125.374
 371 402	96.598
 372 403	69.694
 373 404	99.999
 374 405	54.688
 375 406	67.573
 376 407	92.638
 377 408	60.926
 378 409	132.781
 379 410	104.240
 380 411	118.139
 381 412	102.776
 382 413	86.827
 383 414	65.660
 384 415	109.193
 385 416	54.635
 386 417	121.890
 387 418	65.289
 388 419	151.173
 389 420	54.048
 390 421	134.402
 391 422	86.580
 392 423	91.213
 393 424	101.401
 394 425	73.722
 395 426	89.963
 396 427	107.451
 397 428	107.638
 398 429	71.417
 399 430	84.187
 400 431	157.732
 401 432	56.324
 402 433	154.611
 403 434	79.417
 404 435	84.714
 405 436	118.875
 406 437	89.299
 407 438	118.344
 408 439	174.658
 409 440	60.033
 410 441	65.057
 411 442	72.630
 412 443	176.987
 413 444	123.458
 414 445	123.737
 415 446	122.932
 416 447	137.403
 417 448	58.830
 418 449	147.018
 419 450	63.747
 420 451	123.394
 421 452	116.389
 422 453	155.775
 423 454	107.286
 424 455	68.251
 425 456	77.851
 426 457	165.939
 427 458	104.730
 428 459	82.244
 429 460	81.521
 430 461	171.054
 431 462	66.177
 432 463	158.183
 433 464	90.208
 434 465	93.799
 435 466	108.231
 436 467	188.789
 437 468	70.604
 438 469	131.209
 439 470	116.811
 440 471	155.312
 441 472	105.408
 442 473	107.924
 443 474	108.081
 444 475	93.973
 445 476	83.038
 446 477	122.316
 447 478	111.616
 448 479	191.065
 449 480	69.295
 450 481	130.147
 451 482	140.476
 452 483	94.583
 453 484	77.935
 454 485	159.745
 455 486	73.585
 456 487	199.120
 457 488	147.830
 458 489	188.132
 459 490	74.887
 460 491	176.537
 461 492	149.911
 462 493	114.195
 463 494	102.512
 464 495	82.737
 465 496	99.834
 466 497	141.685
 467 498	140.787
 468 499	226.361
 469 500	74.323
 470 501	193.394
 471 502	155.905
 472 503	201.679
 473 504	76.870
 474 505	176.594
 475 506	100.391
 476 507	90.270
 477 508	119.061
 478 509	208.389
 479 510	108.512
 480 511	163.683
 481 512	78.000
 482 513	105.036
 483 514	157.365
 484 515	171.946
 485 516	121.556
 486 517	141.721
 487 518	172.717
 488 519	251.635
 489 520	85.008
 490 521	203.125
 491 522	110.759
 492 523	221.999
 493 524	168.590
 494 525	100.535
 495 526	129.827
 496 527	133.870
 497 528	86.281
 498 529	133.910
 499 530	155.531
 500 531	144.458
 501 532	106.503
 502 533	164.880
 503 534	154.343
 504 535	186.231
 505 536	169.630
 506 537	263.638
 507 538	134.501
 508 539	97.087
 509 540	100.199
 510 541	239.638
 511 542	182.515
 512 543	240.795
 513 544	106.521
 514 545	206.043
 515 546	91.294
 516 547	234.968
 517 548	153.393
 518 549	177.863
 519 550	94.760
 520 551	144.243
 521 552	115.947
 522 553	171.186
 523 554	153.582
 524 555	172.391
 525 556	160.164
 526 557	278.700
 527 558	124.360
 528 559	149.214
 529 560	94.480
 530 561	122.862
 531 562	153.803
 532 563	283.362
 533 564	162.851
 534 565	210.885
 535 566	153.972
 536 567	105.127
 537 568	157.666
 538 569	273.579
 539 570	120.927
 540 571	256.017
 541 572	104.716
 542 573	252.664
 543 574	206.647
 544 575	132.231
 545 576	97.024
 546 577	267.934
 547 578	143.500
 548 579	277.816
 549 580	139.508
 550 581	211.386
 551 582	207.168
 552 583	190.759
 553 584	210.818
 554 585	116.378
 555 586	162.733
 556 587	298.773
 557 588	105.581
 558 589	167.884
 559 590	168.385
 560 591	259.621
 561 592	225.937
 562 593	291.268
 563 594	116.274
 564 595	139.465
 565 596	162.815
 566 597	267.667
 567 598	143.039
 568 599	318.942
 569 600	107.888
 570 601	295.118
 571 602	158.595
 572 603	206.049
 573 604	217.217
 574 605	131.870
 575 606	224.605
 576 607	348.720
 577 608	136.711
 578 609	154.157
 579 610	234.696
 580 611	195.034
 581 612	135.860
 582 613	295.572
 583 614	220.933
 584 615	213.438
 585 616	117.220
 586 617	291.460
 587 618	213.886
 588 619	349.504
 589 620	162.969
 590 621	159.786
 591 622	228.185
 592 623	248.117
 593 624	120.481
 594 625	140.241
 595 626	237.735
 596 627	160.212
 597 628	179.104
 598 629	256.517
 599 630	130.181
 600 631	281.144
 601 632	195.939
 602 633	308.937
 603 634	199.870
 604 635	256.142
 605 636	225.719
 606 637	138.252
 607 638	172.975
 608 639	236.053
 609 640	116.936
 610 641	325.857
 611 642	226.565
 612 643	360.283
 613 644	165.745
 614 645	194.336
 615 646	186.138
 616 647	364.676
 617 648	127.066
 618 649	223.882
 619 650	135.507
 620 651	181.139
 621 652	280.019
 622 653	404.535
 623 654	265.261
 624 655	305.316
 625 656	269.406
 626 657	270.952
 627 658	224.733
 628 659	398.392
 629 660	136.995
 630 661	353.602
 631 662	255.246
 632 663	176.276
 633 664	245.797
 634 665	167.083
 635 666	285.963
 636 667	222.216
 637 668	233.071
 638 669	411.615
 639 670	263.849
 640 671	280.817
 641 672	137.914
 642 673	352.575
 643 674	263.570
 644 675	154.794
 645 676	150.296
 646 677	407.891
 647 678	268.740
 648 679	324.868
 649 680	174.219
 650 681	425.389
 651 682	201.343
 652 683	414.254
 653 684	176.306
 654 685	326.359
 655 686	139.765
 656 687	373.343
 657 688	215.741
 658 689	262.834
 659 690	190.270
 660 691	390.865
 661 692	242.687
 662 693	168.351
 663 694	233.300
 664 695	331.825
 665 696	196.289
 666 697	308.773
 667 698	239.844
 668 699	413.173
 669 700	146.902
 670 701	369.469
 671 702	153.204
 672 703	320.200
 673 704	156.467
 674 705	259.661
 675 706	266.475
 676 707	342.449
 677 708	245.385
 678 709	433.539
 679 710	255.794
 680 711	285.337
 681 712	273.271
 682 713	253.732
 683 714	178.364
 684 715	179.974
 685 716	258.582
 686 717	417.582
 687 718	252.111
 688 719	444.624
 689 720	158.981
 690 721	342.927
 691 722	230.335
 692 723	423.340
 693 724	322.972
 694 725	219.587
 695 726	161.653
 696 727	500.954
 697 728	163.257
 698 729	178.240
 699 730	330.089
 700 731	274.648
 701 732	338.492
 702 733	472.237
 703 734	279.485
 704 735	181.017
 705 736	206.861
 706 737	324.813
 707 738	341.441
 708 739	459.635
 709 740	351.049
 710 741	217.086
 711 742	304.326
 712 743	482.695
 713 744	230.257
 714 745	373.296
 715 746	286.336
 716 747	343.257
 717 748	224.399
 718 749	366.234
 719 750	170.058
 720 751	455.766
 721 752	290.530
 722 753	450.205
 723 754	228.560
 724 755	429.061
 725 756	175.303
 726 757	415.059
 727 758	267.228
 728 759	248.500
 729 760	227.134
 730 761	453.005
 731 762	270.256
 732 763	412.221
 733 764	265.518
 734 765	224.447
 735 766	278.946
 736 767	327.301
 737 768	173.421
 738 769	485.746
 739 770	198.074
 740 771	481.700
 741 772	400.485
 742 773	515.885
 743 774	285.750
 744 775	265.985
 745 776	389.467
 746 777	360.299
 747 778	294.484
 748 779	405.545
 749 780	205.262
 750 781	374.422
 751 782	294.383
 752 783	272.748
 753 784	198.409
 754 785	447.660
 755 786	416.965
 756 787	540.348
 757 788	297.964
 758 789	576.315
 759 790	329.444
 760 791	426.524
 761 792	214.535
 762 793	404.383
 763 794	318.287
 764 795	360.978
 765 796	304.767
 766 797	546.087
 767 798	247.913
 768 799	402.409
 769 800	213.451
 770 801	432.729
 771 802	418.684
 772 803	453.097
 773 804	410.355
 774 805	280.332
 775 806	297.535
 776 807	603.353
 777 808	432.620
 778 809	602.677
 779 810	228.858
 780 811	566.766
 781 812	285.659
 782 813	562.294
 783 814	469.205
 784 815	539.961
 785 816	276.719
 786 817	375.781
 787 818	337.721
 788 819	250.157
 789 820	445.796
 790 821	597.919
 791 822	379.863
 792 823	599.584
 793 824	388.805
 794 825	259.694
 795 826	356.439
 796 827	612.754
 797 828	285.057
 798 829	559.433
 799 830	413.020
 800 831	593.672
 801 832	249.909
 802 833	282.154
 803 834	391.591
 804 835	548.075
 805 836	300.995
 806 837	327.528
 807 838	357.640
 808 839	630.147
 809 840	240.085
 810 841	385.622
 811 842	361.051
 812 843	556.864
 813 844	385.684
 814 845	280.992
 815 846	397.956
 816 847	273.946
 817 848	422.667
 818 849	660.911
 819 850	296.792
 820 851	505.192
 821 852	382.088
 822 853	649.869
 823 854	489.156
 824 855	308.867
 825 856	425.056
 826 857	668.227
 827 858	268.006
 828 859	657.121
 829 860	398.152
 830 861	452.340
 831 862	368.684
 832 863	656.103
 833 864	261.988
 834 865	729.809
 835 866	489.096
 836 867	351.880
 837 868	334.404
 838 869	474.241
 839 870	338.563
 840 871	459.033
 841 872	497.209
 842 873	534.065
 843 874	372.299
 844 875	279.762
 845 876	491.375
 846 877	710.078
 847 878	413.449
 848 879	696.140
 849 880	272.079
 850 881	609.093
 851 882	258.467
 852 883	596.331
 853 884	322.056
 854 885	437.682
 855 886	417.652
 856 887	731.995
 857 888	521.343
 858 889	530.866
 859 890	465.394
 860 891	299.056
 861 892	527.770
 862 893	504.228
 863 894	409.203
 864 895	766.884
 865 896	285.538
 866 897	393.334
 867 898	447.089
 868 899	470.546
 869 900	301.585
 870 901	540.807
 871 902	578.301
 872 903	422.965
 873 904	522.254
 874 905	713.056
 875 906	532.494
 876 907	804.016
 877 908	470.847
 878 909	615.902
 879 910	296.275
 880 911	738.584
 881 912	349.982
 882 913	567.343
 883 914	427.959
 884 915	558.470
 885 916	486.113
 886 917	653.222
 887 918	354.391
 888 919	753.502
 889 920	384.491
 890 921	783.713
 891 922	444.425
 892 923	555.907
 893 924	315.381
 894 925	534.025
 895 926	457.225
 896 927	612.445
 897 928	415.432
 898 929	810.883
 899 930	412.462
 900 931	374.656
 901 932	496.908
 902 933	857.887
 903 934	469.074
 904 935	401.654
 905 936	321.207
 906 937	750.433
 907 938	555.843
 908 939	760.428
 909 940	511.905
 910 941	810.391
 911 942	453.356
 912 943	632.770
 913 944	475.663
 914 945	355.380
 915 946	443.771
 916 947	802.638
 917 948	479.495
 918 949	636.489
 919 950	383.419
 920 951	786.818
 921 952	400.448
 922 953	741.216
 923 954	562.524
 924 955	770.668
 925 956	520.002
 926 957	448.112
 927 958	477.560
 928 959	696.611
 929 960	342.631
 930 961	538.857
 931 962	667.437
 932 963	669.039
 933 964	646.306
 934 965	839.498
 935 966	421.424
 936 967	826.907
 937 968	379.094
 938 969	490.814
 939 970	646.570
 940 971	925.122
 941 972	338.900
 942 973	713.960
 943 974	648.768
 944 975	395.063
 945 976	678.834
 946 977	892.736
 947 978	686.564
 948 979	710.641
 949 980	344.891
 950 981	725.068
 951 982	535.355
 952 983	905.818
 953 984	669.426
 954 985	770.775
 955 986	516.285
 956 987	576.764
 957 988	438.111
 958 989	601.620
 959 990	378.879
 960 991	803.100
 961 992	502.742
 962 993	849.541
 963 994	570.375
 964 995	815.114
 965 996	623.462
 966 997	945.200
 967 998	563.641
 968 999	642.479
 969 1000	370.964
 970 1001	432.790
 971 1002	600.533
 972 1003	661.045
 973 1004	683.520
 974 1005	660.095
 975 1006	505.568
 976 1007	697.578
 977 1008	363.957
 978 1009	866.210
 979 1010	711.547
 980 1011	886.800
 981 1012	483.252
 982 1013	975.738
 983 1014	397.548
 984 1015	497.066
 985 1016	543.594
 986 1017	750.302
 987 1018	521.405
 988 1019	910.082
 989 1020	466.539
 990 1021	880.295
 991 1022	727.429
 992 1023	572.192

time_vs_size.txt

EMAN2/BoxSize (last edited 2021-10-15 17:30:25 by SteveLudtke)