Changeset 706f466 in sasmodels
- Timestamp:
- Sep 28, 2017 4:40:23 PM (7 years ago)
- Branches:
- master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
- Children:
- 6ab64c9
- Parents:
- 62d7601
- Files:
-
- 2 edited
Legend:
- Unmodified
- Added
- Removed
-
doc/genapi.py
r2e66ef5 r706f466 59 59 #('alignment', 'GPU data alignment [unused]'), 60 60 ('bumps_model', 'Bumps interface'), 61 ('compare', 'Compare models on different compute engines'), 61 62 ('compare_many', 'Batch compare models on different compute engines'), 62 ('co mpare', 'Compare models on different compute engines'),63 ('conversion_table', 'Model conversion table'), 63 64 ('convert', 'Sasview to sasmodel converter'), 64 65 ('core', 'Model access'), … … 82 83 ('sasview_model', 'Sasview interface'), 83 84 ('sesans', 'SESANS calculation routines'), 85 ('special', 'Special functions library'), 84 86 ('weights', 'Distribution functions'), 85 87 ] -
sasmodels/special.py
r4f611f1 r706f466 1 """1 r""" 2 2 Special Functions 3 3 ................. … … 10 10 M_PI, M_PI_2, M_PI_4, M_SQRT1_2, M_E: 11 11 $\pi$, $\pi/2$, $\pi/4$, $1/\sqrt{2}$ and Euler's constant $e$ 12 12 13 exp, log, pow(x,y), expm1, sqrt: 13 14 Power functions $e^x$, $\ln x$, $x^y$, $e^x - 1$, $\sqrt{x}$. 14 15 The function expm1(x) is accurate across all $x$, including $x$ 15 16 very close to zero. 17 16 18 sin, cos, tan, asin, acos, atan: 17 19 Trigonometry functions and inverses, operating on radians. 20 18 21 sinh, cosh, tanh, asinh, acosh, atanh: 19 22 Hyperbolic trigonometry functions. 23 20 24 atan2(y,x): 21 25 Angle from the $x$\ -axis to the point $(x,y)$, which is equal to … … 24 28 atan(y/x) would return a value in quadrant I. Similarly for 25 29 quadrants II and IV when $x$ and $y$ have opposite sign. 30 26 31 fmin(x,y), fmax(x,y), trunc, rint: 27 32 Floating point functions. rint(x) returns the nearest integer. 33 28 34 NAN: 29 35 NaN, Not a Number, $0/0$. Use isnan(x) to test for NaN. Note that 30 36 you cannot use :code:`x == NAN` to test for NaN values since that 31 37 will always return false. NAN does not equal NAN! 38 32 39 INFINITY: 33 40 $\infty, 1/0$. Use isinf(x) to test for infinity, or isfinite(x) 34 41 to test for finite and not NaN. 42 35 43 erf, erfc, tgamma, lgamma: **do not use** 36 44 Special functions that should be part of the standard, but are missing … … 42 50 M_PI_180, M_4PI_3: 43 51 $\frac{\pi}{180}$, $\frac{4\pi}{3}$ 52 44 53 SINCOS(x, s, c): 45 54 Macro which sets s=sin(x) and c=cos(x). The variables *c* and *s* 46 55 must be declared first. 56 47 57 square(x): 48 58 $x^2$ 59 49 60 cube(x): 50 61 $x^3$ 62 51 63 sas_sinx_x(x): 52 64 $\sin(x)/x$, with limit $\sin(0)/0 = 1$. 65 53 66 powr(x, y): 54 67 $x^y$ for $x \ge 0$; this is faster than general $x^y$ on some GPUs. 68 55 69 pown(x, n): 56 70 $x^n$ for $n$ integer; this is faster than general $x^n$ on some GPUs. 71 57 72 FLOAT_SIZE: 58 73 The number of bytes in a floating point value. Even though all … … 67 82 ... code for single precision ... 68 83 #endif 84 69 85 SAS_DOUBLE: 70 86 A replacement for :code:`double` so that the declared variable will … … 88 104 sorted from highest to lowest. 89 105 90 :code:`source = ["lib/polevl.c", ...]` (`link to code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/polevl.c>`_)91 92 106 p1evl(x, c, n): 93 107 Evaluation of normalized polynomial $p(x) = x^n + \sum_{i=0}^{n-1} c_i x^i$ … … 97 111 sorted from highest to lowest. 98 112 99 :code:`source = ["lib/polevl.c", ...]`100 (`link to code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/polevl.c>`_)101 102 113 sas_gamma(x): 103 114 Gamma function $\text{sas_gamma}(x) = \Gamma(x)$. … … 105 116 The standard math function, tgamma(x) is unstable for $x < 1$ 106 117 on some platforms. 107 108 :code:`source = ["lib/sasgamma.c", ...]`109 (`link to code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gamma.c>`_)110 118 111 119 sas_erf(x), sas_erfc(x): … … 118 126 on some platforms. 119 127 120 :code:`source = ["lib/polevl.c", "lib/sas_erf.c", ...]`121 (`link to error functions' code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_erf.c>`_)122 123 128 sas_J0(x): 124 129 Bessel function of the first kind $\text{sas_J0}(x)=J_0(x)$ where … … 127 132 The standard math function j0(x) is not available on all platforms. 128 133 129 :code:`source = ["lib/polevl.c", "lib/sas_J0.c", ...]`130 (`link to Bessel function's code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_J0.c>`_)131 132 134 sas_J1(x): 133 135 Bessel function of the first kind $\text{sas_J1}(x)=J_1(x)$ where … … 135 137 136 138 The standard math function j1(x) is not available on all platforms. 137 138 :code:`source = ["lib/polevl.c", "lib/sas_J1.c", ...]`139 (`link to Bessel function's code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_J1.c>`_)140 139 141 140 sas_JN(n, x): … … 147 146 The standard math function jn(n, x) is not available on all platforms. 148 147 149 :code:`source = ["lib/polevl.c", "lib/sas_J0.c", "lib/sas_J1.c", "lib/sas_JN.c", ...]`150 (`link to Bessel function's code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_JN.c>`_)151 152 148 sas_Si(x): 153 149 Sine integral $\text{Si}(x) = \int_0^x \tfrac{\sin t}{t}\,dt$. … … 170 166 - \frac{x^3}{3\times 3!} + \frac{x^5}{5 \times 5!} - \frac{x^7}{7 \times 7!} 171 167 + \frac{x^9}{9\times 9!} - \frac{x^{11}}{11\times 11!} 172 173 :code:`source = ["lib/Si.c", ...]`174 (`link to code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/Si.c>`_)175 168 176 169 sas_3j1x_x(x): … … 182 175 This function uses a Taylor series for small $x$ for numerical accuracy. 183 176 184 :code:`source = ["lib/sas_3j1x_x.c", ...]`185 (`link to code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_3j1x_x.c>`_)186 187 177 188 178 sas_2J1x_x(x): … … 191 181 and first order. 192 182 193 :code:`source = ["lib/polevl.c", "lib/sas_J1.c", ...]`194 (`link to Bessel form's code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_J1.c>`_)195 196 183 197 184 Gauss76Z[i], Gauss76Wt[i]: … … 201 188 Similar arrays are available in :code:`gauss20.c` for 20-point 202 189 quadrature and in :code:`gauss150.c` for 150-point quadrature. 203 204 :code:`source = ["lib/gauss76.c", ...]`205 (`link to code <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/gauss76.c>`_)206 207 190 208 191 """ … … 290 273 retvalue = (sin(x) - x*cos(x))/x**2 291 274 retvalue[x == 0.] = 0. 292 275 293 276 def sas_3j1x_x(x): 294 277 if np.isscalar(x): … … 313 296 314 297 Gauss20Wt = np.array([ 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 298 .0176140071391521, 299 .0406014298003869, 300 .0626720483341091, 301 .0832767415767047, 302 .10193011981724, 303 .118194531961518, 304 .131688638449177, 305 .142096109318382, 306 .149172986472604, 307 .152753387130726, 308 .152753387130726, 309 .149172986472604, 310 .142096109318382, 311 .131688638449177, 312 .118194531961518, 313 .10193011981724, 314 .0832767415767047, 315 .0626720483341091, 316 .0406014298003869, 317 .0176140071391521 335 318 ]) 336 319 337 320 Gauss20Z = np.array([ 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 321 -.993128599185095, 322 -.963971927277914, 323 -.912234428251326, 324 -.839116971822219, 325 -.746331906460151, 326 -.636053680726515, 327 -.510867001950827, 328 -.37370608871542, 329 -.227785851141645, 330 -.076526521133497, 331 .0765265211334973, 332 .227785851141645, 333 .37370608871542, 334 .510867001950827, 335 .636053680726515, 336 .746331906460151, 337 .839116971822219, 338 .912234428251326, 339 .963971927277914, 340 .993128599185095 358 341 ]) 359 342 360 343 Gauss76Wt = np.array([ 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 344 .00126779163408536, #0 345 .00294910295364247, 346 .00462793522803742, 347 .00629918049732845, 348 .00795984747723973, 349 .00960710541471375, 350 .0112381685696677, 351 .0128502838475101, 352 .0144407317482767, 353 .0160068299122486, 354 .0175459372914742, #10 355 .0190554584671906, 356 .020532847967908, 357 .0219756145344162, 358 .0233813253070112, 359 .0247476099206597, 360 .026072164497986, 361 .0273527555318275, 362 .028587223650054, 363 .029773487255905, 364 .0309095460374916, #20 365 .0319934843404216, 366 .0330234743977917, 367 .0339977794120564, 368 .0349147564835508, 369 .0357728593807139, 370 .0365706411473296, 371 .0373067565423816, 372 .0379799643084053, 373 .0385891292645067, 374 .0391332242205184, #30 375 .0396113317090621, 376 .0400226455325968, 377 .040366472122844, 378 .0406422317102947, 379 .0408494593018285, 380 .040987805464794, 381 .0410570369162294, 382 .0410570369162294, 383 .040987805464794, 384 .0408494593018285, #40 385 .0406422317102947, 386 .040366472122844, 387 .0400226455325968, 388 .0396113317090621, 389 .0391332242205184, 390 .0385891292645067, 391 .0379799643084053, 392 .0373067565423816, 393 .0365706411473296, 394 .0357728593807139, #50 395 .0349147564835508, 396 .0339977794120564, 397 .0330234743977917, 398 .0319934843404216, 399 .0309095460374916, 400 .029773487255905, 401 .028587223650054, 402 .0273527555318275, 403 .026072164497986, 404 .0247476099206597, #60 405 .0233813253070112, 406 .0219756145344162, 407 .020532847967908, 408 .0190554584671906, 409 .0175459372914742, 410 .0160068299122486, 411 .0144407317482767, 412 .0128502838475101, 413 .0112381685696677, 414 .00960710541471375, #70 415 .00795984747723973, 416 .00629918049732845, 417 .00462793522803742, 418 .00294910295364247, 419 .00126779163408536 #75 (indexed from 0) 437 420 ]) 438 421 439 422 Gauss76Z = np.array([ 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 423 -.999505948362153, #0 424 -.997397786355355, 425 -.993608772723527, 426 -.988144453359837, 427 -.981013938975656, 428 -.972229228520377, 429 -.961805126758768, 430 -.949759207710896, 431 -.936111781934811, 432 -.92088586125215, 433 -.904107119545567, #10 434 -.885803849292083, 435 -.866006913771982, 436 -.844749694983342, 437 -.822068037328975, 438 -.7980001871612, 439 -.77258672828181, 440 -.74587051350361, 441 -.717896592387704, 442 -.688712135277641, 443 -.658366353758143, #20 444 -.626910417672267, 445 -.594397368836793, 446 -.560882031601237, 447 -.526420920401243, 448 -.491072144462194, 449 -.454895309813726, 450 -.417951418780327, 451 -.380302767117504, 452 -.342012838966962, 453 -.303146199807908, #30 454 -.263768387584994, 455 -.223945802196474, 456 -.183745593528914, 457 -.143235548227268, 458 -.102483975391227, 459 -.0615595913906112, 460 -.0205314039939986, 461 .0205314039939986, 462 .0615595913906112, 463 .102483975391227, #40 464 .143235548227268, 465 .183745593528914, 466 .223945802196474, 467 .263768387584994, 468 .303146199807908, 469 .342012838966962, 470 .380302767117504, 471 .417951418780327, 472 .454895309813726, 473 .491072144462194, #50 474 .526420920401243, 475 .560882031601237, 476 .594397368836793, 477 .626910417672267, 478 .658366353758143, 479 .688712135277641, 480 .717896592387704, 481 .74587051350361, 482 .77258672828181, 483 .7980001871612, #60 484 .822068037328975, 485 .844749694983342, 486 .866006913771982, 487 .885803849292083, 488 .904107119545567, 489 .92088586125215, 490 .936111781934811, 491 .949759207710896, 492 .961805126758768, 493 .972229228520377, #70 494 .981013938975656, 495 .988144453359837, 496 .993608772723527, 497 .997397786355355, 498 .999505948362153 #75 516 499 ]) 517 500 518 501 Gauss150Z = np.array([ 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 502 -0.9998723404457334, 503 -0.9993274305065947, 504 -0.9983473449340834, 505 -0.9969322929775997, 506 -0.9950828645255290, 507 -0.9927998590434373, 508 -0.9900842691660192, 509 -0.9869372772712794, 510 -0.9833602541697529, 511 -0.9793547582425894, 512 -0.9749225346595943, 513 -0.9700655145738374, 514 -0.9647858142586956, 515 -0.9590857341746905, 516 -0.9529677579610971, 517 -0.9464345513503147, 518 -0.9394889610042837, 519 -0.9321340132728527, 520 -0.9243729128743136, 521 -0.9162090414984952, 522 -0.9076459563329236, 523 -0.8986873885126239, 524 -0.8893372414942055, 525 -0.8795995893549102, 526 -0.8694786750173527, 527 -0.8589789084007133, 528 -0.8481048644991847, 529 -0.8368612813885015, 530 -0.8252530581614230, 531 -0.8132852527930605, 532 -0.8009630799369827, 533 -0.7882919086530552, 534 -0.7752772600680049, 535 -0.7619248049697269, 536 -0.7482403613363824, 537 -0.7342298918013638, 538 -0.7198995010552305, 539 -0.7052554331857488, 540 -0.6903040689571928, 541 -0.6750519230300931, 542 -0.6595056411226444, 543 -0.6436719971150083, 544 -0.6275578900977726, 545 -0.6111703413658551, 546 -0.5945164913591590, 547 -0.5776035965513142, 548 -0.5604390262878617, 549 -0.5430302595752546, 550 -0.5253848818220803, 551 -0.5075105815339176, 552 -0.4894151469632753, 553 -0.4711064627160663, 554 -0.4525925063160997, 555 -0.4338813447290861, 556 -0.4149811308476706, 557 -0.3959000999390257, 558 -0.3766465660565522, 559 -0.3572289184172501, 560 -0.3376556177463400, 561 -0.3179351925907259, 562 -0.2980762356029071, 563 -0.2780873997969574, 564 -0.2579773947782034, 565 -0.2377549829482451, 566 -0.2174289756869712, 567 -0.1970082295132342, 568 -0.1765016422258567, 569 -0.1559181490266516, 570 -0.1352667186271445, 571 -0.1145563493406956, 572 -0.0937960651617229, 573 -0.0729949118337358, 574 -0.0521619529078925, 575 -0.0313062657937972, 576 -0.0104369378042598, 577 0.0104369378042598, 578 0.0313062657937972, 579 0.0521619529078925, 580 0.0729949118337358, 581 0.0937960651617229, 582 0.1145563493406956, 583 0.1352667186271445, 584 0.1559181490266516, 585 0.1765016422258567, 586 0.1970082295132342, 587 0.2174289756869712, 588 0.2377549829482451, 589 0.2579773947782034, 590 0.2780873997969574, 591 0.2980762356029071, 592 0.3179351925907259, 593 0.3376556177463400, 594 0.3572289184172501, 595 0.3766465660565522, 596 0.3959000999390257, 597 0.4149811308476706, 598 0.4338813447290861, 599 0.4525925063160997, 600 0.4711064627160663, 601 0.4894151469632753, 602 0.5075105815339176, 603 0.5253848818220803, 604 0.5430302595752546, 605 0.5604390262878617, 606 0.5776035965513142, 607 0.5945164913591590, 608 0.6111703413658551, 609 0.6275578900977726, 610 0.6436719971150083, 611 0.6595056411226444, 612 0.6750519230300931, 613 0.6903040689571928, 614 0.7052554331857488, 615 0.7198995010552305, 616 0.7342298918013638, 617 0.7482403613363824, 618 0.7619248049697269, 619 0.7752772600680049, 620 0.7882919086530552, 621 0.8009630799369827, 622 0.8132852527930605, 623 0.8252530581614230, 624 0.8368612813885015, 625 0.8481048644991847, 626 0.8589789084007133, 627 0.8694786750173527, 628 0.8795995893549102, 629 0.8893372414942055, 630 0.8986873885126239, 631 0.9076459563329236, 632 0.9162090414984952, 633 0.9243729128743136, 634 0.9321340132728527, 635 0.9394889610042837, 636 0.9464345513503147, 637 0.9529677579610971, 638 0.9590857341746905, 639 0.9647858142586956, 640 0.9700655145738374, 641 0.9749225346595943, 642 0.9793547582425894, 643 0.9833602541697529, 644 0.9869372772712794, 645 0.9900842691660192, 646 0.9927998590434373, 647 0.9950828645255290, 648 0.9969322929775997, 649 0.9983473449340834, 650 0.9993274305065947, 651 0.9998723404457334 669 652 ]) 670 653 671 654 Gauss150Wt = np.array([ 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 655 0.0003276086705538, 656 0.0007624720924706, 657 0.0011976474864367, 658 0.0016323569986067, 659 0.0020663664924131, 660 0.0024994789888943, 661 0.0029315036836558, 662 0.0033622516236779, 663 0.0037915348363451, 664 0.0042191661429919, 665 0.0046449591497966, 666 0.0050687282939456, 667 0.0054902889094487, 668 0.0059094573005900, 669 0.0063260508184704, 670 0.0067398879387430, 671 0.0071507883396855, 672 0.0075585729801782, 673 0.0079630641773633, 674 0.0083640856838475, 675 0.0087614627643580, 676 0.0091550222717888, 677 0.0095445927225849, 678 0.0099300043714212, 679 0.0103110892851360, 680 0.0106876814158841, 681 0.0110596166734735, 682 0.0114267329968529, 683 0.0117888704247183, 684 0.0121458711652067, 685 0.0124975796646449, 686 0.0128438426753249, 687 0.0131845093222756, 688 0.0135194311690004, 689 0.0138484622795371, 690 0.0141714592928592, 691 0.0144882814685445, 692 0.0147987907597169, 693 0.0151028518701744, 694 0.0154003323133401, 695 0.0156911024699895, 696 0.0159750356447283, 697 0.0162520081211971, 698 0.0165218992159766, 699 0.0167845913311726, 700 0.0170399700056559, 701 0.0172879239649355, 702 0.0175283451696437, 703 0.0177611288626114, 704 0.0179861736145128, 705 0.0182033813680609, 706 0.0184126574807331, 707 0.0186139107660094, 708 0.0188070535331042, 709 0.0189920016251754, 710 0.0191686744559934, 711 0.0193369950450545, 712 0.0194968900511231, 713 0.0196482898041878, 714 0.0197911283358190, 715 0.0199253434079123, 716 0.0200508765398072, 717 0.0201676730337687, 718 0.0202756819988200, 719 0.0203748563729175, 720 0.0204651529434560, 721 0.0205465323660984, 722 0.0206189591819181, 723 0.0206824018328499, 724 0.0207368326754401, 725 0.0207822279928917, 726 0.0208185680053983, 727 0.0208458368787627, 728 0.0208640227312962, 729 0.0208731176389954, 730 0.0208731176389954, 731 0.0208640227312962, 732 0.0208458368787627, 733 0.0208185680053983, 734 0.0207822279928917, 735 0.0207368326754401, 736 0.0206824018328499, 737 0.0206189591819181, 738 0.0205465323660984, 739 0.0204651529434560, 740 0.0203748563729175, 741 0.0202756819988200, 742 0.0201676730337687, 743 0.0200508765398072, 744 0.0199253434079123, 745 0.0197911283358190, 746 0.0196482898041878, 747 0.0194968900511231, 748 0.0193369950450545, 749 0.0191686744559934, 750 0.0189920016251754, 751 0.0188070535331042, 752 0.0186139107660094, 753 0.0184126574807331, 754 0.0182033813680609, 755 0.0179861736145128, 756 0.0177611288626114, 757 0.0175283451696437, 758 0.0172879239649355, 759 0.0170399700056559, 760 0.0167845913311726, 761 0.0165218992159766, 762 0.0162520081211971, 763 0.0159750356447283, 764 0.0156911024699895, 765 0.0154003323133401, 766 0.0151028518701744, 767 0.0147987907597169, 768 0.0144882814685445, 769 0.0141714592928592, 770 0.0138484622795371, 771 0.0135194311690004, 772 0.0131845093222756, 773 0.0128438426753249, 774 0.0124975796646449, 775 0.0121458711652067, 776 0.0117888704247183, 777 0.0114267329968529, 778 0.0110596166734735, 779 0.0106876814158841, 780 0.0103110892851360, 781 0.0099300043714212, 782 0.0095445927225849, 783 0.0091550222717888, 784 0.0087614627643580, 785 0.0083640856838475, 786 0.0079630641773633, 787 0.0075585729801782, 788 0.0071507883396855, 789 0.0067398879387430, 790 0.0063260508184704, 791 0.0059094573005900, 792 0.0054902889094487, 793 0.0050687282939456, 794 0.0046449591497966, 795 0.0042191661429919, 796 0.0037915348363451, 797 0.0033622516236779, 798 0.0029315036836558, 799 0.0024994789888943, 800 0.0020663664924131, 801 0.0016323569986067, 802 0.0011976474864367, 803 0.0007624720924706, 804 0.0003276086705538 822 805 ])
Note: See TracChangeset
for help on using the changeset viewer.