//  (C) Copyright John Maddock 2006.
//  Use, modification and distribution are subject to the
//  Boost Software License, Version 1.0. (See accompanying file
//  LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

#include <boost/math/bindings/rr.hpp>
#include <boost/math/constants/constants.hpp>
#include <boost/math/tools/precision.hpp>
#include <boost/math/tools/test.hpp>
#include <boost/math/tools/polynomial.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/operation.hpp>
#include <boost/cstdint.hpp>
#include <boost/bind.hpp>
#include <boost/math/special_functions/log1p.hpp>
#include <boost/lexical_cast.hpp>
#include <boost/array.hpp>
#include <boost/test/included/test_exec_monitor.hpp>

//
// this is a sort of recursive include, since this file
// is used to create the contents of gamma.hpp.
// However, we will be using the "generic" version of gamma
// which doesn't use a lanczos approximation.
//
#include <boost/math/special_functions/gamma.hpp>

#include <iostream>
#include <iomanip>
#include <vector>

using boost::numeric::ublas::matrix;
using boost::numeric::ublas::vector;

struct lanczos_spot_data
{
   int N;
   double g;
   double err;
};

lanczos_spot_data sweet_spots[] = {
// a few selected spots from Pugh's thesis:
3, 2.03209, 6.4e-7,
4, 3.655180, 8.5e-8,
9, 8.406094, 6.9e-15,
10, 9.656578, 2.1e-16,
11, 10.900511, 6.1e-18,
12, 12.066012, 1.1e-19,
13, 13.144565, 5.2e-21,
14, 13.726821, 4.0e-22,
15, 14.977863, 1.2e-23,
20, 20.298892, 7.8e-31,
21, 21.508926, 2.1e-32,
22, 22.618910, 1.8e-34,
23, 23.118012, 5.2e-35,

// some more we've found, these are all the points where the first
// negleted term from the Lanczos series changes sign, there is one 
// point just above that point, and one just below:

3, 0.58613894134759903, 0.00036580426080686315,
3, 0.58613894879817963, 0.00036580416320668711,
3, 1.249951496720314, 4.2305536798953929e-005,
3, 1.2499515041708946, 4.2305534015367749e-005,
3, 1.8384982049465179, 1.164778449938813e-005,
3, 1.8384982123970985, 1.1647786529958048e-005,
3, 2.3276706635951996, 5.3732582230468998e-006,
3, 2.3276706710457802, 5.3732600294516588e-006,
3, 2.6038404405117035, 7.4060771171390379e-007,
3, 2.6038404479622841, 7.406058543248471e-007,
4, 0.62704569846391678, 0.00012800533557793425,
4, 0.62704570591449738, 0.00012800531551557324,
4, 1.3344274088740349, 9.0166140418663663e-006,
4, 1.3344274163246155, 9.0166116338646694e-006,
4, 1.9880444705486298, 1.4955557778725745e-006,
4, 1.9880444779992104, 1.4955557079994109e-006,
4, 2.5835030898451805, 4.5877676859600527e-007,
4, 2.5835030972957611, 4.5877667876325684e-007,
4, 3.1086832508444786, 2.1441725587707906e-007,
4, 3.1086832582950592, 2.1441723688775765e-007,
4, 3.5476611852645874, 1.0813760990515544e-007,
4, 3.547661192715168, 1.0813766234070327e-007,
4, 3.6492579728364944, 1.047642251124206e-007,
4, 3.649257980287075, 1.0476415796074403e-007,
5, 0.65435918420553207, 5.5274429026631278e-005,
5, 0.65435919165611267, 5.527441185903385e-005,
5, 1.389187254011631, 2.6307833416554052e-006,
5, 1.3891872614622116, 2.6307826816540255e-006,
5, 2.0801975876092911, 2.9014694764756466e-007,
5, 2.0801975950598717, 2.9014696142909125e-007,
5, 2.7272208333015442, 5.9176544536206063e-008,
5, 2.7272208407521248, 5.9176528019009275e-008,
5, 3.3269794136285782, 1.9379184595472579e-008,
5, 3.3269794210791588, 1.9379189057179851e-008,
5, 3.8722873777151108, 9.0123694745032944e-009,
5, 3.8722873851656914, 9.0123661007867372e-009,
5, 4.3516943231225014, 4.9816097608842378e-009,
5, 4.351694330573082, 4.9816087871575021e-009,
6, 0.67425807565450668, 2.7371903086326379e-005,
6, 0.67425808310508728, 2.7371893712684938e-005,
6, 1.4284561350941658, 9.4138428977455083e-007,
6, 1.4284561425447464, 9.4138417896969612e-007,
6, 2.1447814926505089, 7.4109049995920916e-008,
6, 2.1447815001010895, 7.4109044064461768e-008,
6, 2.8244904428720474, 1.0708333024058614e-008,
6, 2.824490450322628, 1.0708333661155651e-008,
6, 3.4669468700885773, 2.4940116660002578e-009,
6, 3.4669468775391579, 2.4940118560592828e-009,
6, 4.0696377158164978, 8.5267622527507512e-010,
6, 4.0696377232670784, 8.5267624048985822e-010,
6, 4.6278460025787354, 3.9323405231792876e-010,
6, 4.6278460100293159, 3.9323393550021793e-010,
6, 5.1345816180109978, 2.1943462473419721e-010,
6, 5.1345816254615784, 2.1943458105218707e-010,
6, 5.5917193591594696, 1.242916896389048e-010,
6, 5.5917193666100502, 1.2429175679293363e-010,
7, 0.68959406018257141, 1.4934177419305624e-005,
7, 0.68959406763315201, 1.4934171696393048e-005,
7, 1.4584139510989189, 3.8891482410396904e-007,
7, 1.4584139585494995, 3.8891475436265016e-007,
7, 2.1934122517704964, 2.2977989848570384e-008,
7, 2.193412259221077, 2.2977982819591052e-008,
7, 2.8964230641722679, 2.4742239281355561e-009,
7, 2.8964230716228485, 2.4742234478616864e-009,
7, 3.5678729563951492, 4.2816138965285379e-010,
7, 3.5678729638457298, 4.2816128067858218e-010,
7, 4.2069913893938065, 1.0931003472338519e-010,
7, 4.2069913968443871, 1.0931004659777642e-010,
7, 4.8118086308240891, 3.8518213120095777e-011,
7, 4.8118086382746696, 3.8518212172324096e-011,
7, 5.3790060579776764, 1.7613602961109952e-011,
7, 5.379006065428257, 1.7613603275796266e-011,
7, 5.9039439857006073, 9.7098281187573355e-012,
7, 5.9039439931511879, 9.7098271522398387e-012,
7, 6.3838147521018982, 5.7878801570305524e-012,
7, 6.3838147595524788, 5.7878771973838702e-012,
7, 6.7833591029047966, 2.829092368392454e-012,
7, 6.7833591103553772, 2.8290901754246269e-012,
8, 0.70188850909471512, 8.7610752024780369e-006,
8, 0.70188851654529572, 8.7610704217796693e-006,
8, 1.4822541922330856, 1.7881701068328982e-007,
8, 1.4822541996836662, 1.7881694190606741e-007,
8, 2.231784276664257, 8.2272386690167009e-009,
8, 2.2317842841148376, 8.2272367814664617e-009,
8, 2.9525627419352531, 6.8591536474980462e-010,
8, 2.9525627493858337, 6.8591515664364383e-010,
8, 3.6455128863453865, 9.1549235855884194e-011,
8, 3.6455128937959671, 9.1549217088498416e-011,
8, 4.3106606528162956, 1.8015466799942756e-011,
8, 4.3106606602668762, 1.8015467001952356e-011,
8, 4.9472371935844421, 4.918149362493385e-012,
8, 4.9472372010350227, 4.9181495178774722e-012,
8, 5.553666315972805, 1.7720036810370511e-012,
8, 5.5536663234233856, 1.7720035779693502e-012,
8, 6.1274929493665695, 8.0403304333886761e-013,
8, 6.1274929568171501, 8.0403274526493467e-013,
8, 6.6654317826032639, 4.3551924163930176e-013,
8, 6.6654317900538445, 4.3551918580885179e-013,
8, 7.1646013185381889, 2.6142608803934048e-013,
8, 7.1646013259887695, 2.6142610866873453e-013,
8, 7.6312204822897911, 1.5767278184484503e-013,
8, 7.6312204897403717, 1.576728292094547e-013,
8, 7.8779716044664383, 6.0579161144149095e-014,
8, 7.8779716119170189, 6.0579145495868576e-014,
9, 0.7120363786816597, 5.4383680639900953e-006,
9, 0.7120363861322403, 5.4383647430909934e-006,
9, 1.5018193200230598, 8.9331552269793478e-008,
9, 1.5018193274736404, 8.9331529217486569e-008,
9, 2.2630845010280609, 3.2922940247769628e-009,
9, 2.2630845084786415, 3.2922936681951672e-009,
9, 2.9980199560523033, 2.1887825488935961e-010,
9, 2.9980199635028839, 2.1887818854849753e-010,
9, 3.7078000977635384, 2.3213138957857667e-011,
9, 3.707800105214119, 2.3213132917173771e-011,
9, 4.3928612694144249, 3.6226509291513855e-012,
9, 4.3928612768650055, 3.6226507116549233e-012,
9, 5.0530400574207306, 7.8472620106742312e-013,
9, 5.0530400648713112, 7.8472599177745781e-013,
9, 5.6876183524727821, 2.2542091218064693e-013,
9, 5.6876183599233627, 2.2542085472082343e-013,
9, 6.2953110262751579, 8.261026286619692e-014,
9, 6.2953110337257385, 8.2610267677539022e-014,
9, 6.8742315545678139, 3.7226229880661886e-014,
9, 6.8742315620183945, 3.722621661830273e-014,
9, 7.4219388365745544, 1.9804198141916339e-014,
9, 7.421938844025135, 1.9804197841080089e-014,
9, 7.936030775308609, 1.1800366216622659e-014,
9, 7.9360307827591896, 1.1800362381207757e-014,
9, 8.4175376817584038, 7.341772297716249e-015,
9, 8.4175376892089844, 7.3417744970482286e-015,
10, 0.72060277312994003, 3.5324438420494327e-006,
10, 0.72060278058052063, 3.5324425213780505e-006,
10, 1.5182576552033424, 4.7690979892410133e-008,
10, 1.518257662653923, 4.7690968887170632e-008,
10, 2.2892609387636185, 1.4396365431590603e-009,
10, 2.2892609462141991, 1.4396363283470758e-009,
10, 3.0358340814709663, 7.8118059457005561e-011,
10, 3.0358340889215469, 7.8118048721726738e-011,
10, 3.7592824399471283, 6.7417155809106958e-012,
10, 3.7592824473977089, 6.7417147580480395e-012,
10, 4.4602697342634201, 8.5434024476488055e-013,
10, 4.4602697417140007, 8.5434000006256964e-013,
10, 5.1389683187007904, 1.5015237157428729e-013,
10, 5.138968326151371, 1.5015232503714518e-013,
10, 5.795127771794796, 3.5035278065891386e-014,
10, 5.7951277792453766, 3.5035266685032581e-014,
10, 6.4280949085950851, 1.0474518584578706e-014,
10, 6.4280949160456657, 1.047451900091486e-014,
10, 7.0368044599890709, 3.8902615249132166e-015,
10, 7.0368044674396515, 3.8902605386615012e-015,
10, 7.6197631657123566, 1.742442940023884e-015,
10, 7.6197631731629372, 1.7424426824540998e-015,
10, 8.1750892177224159, 9.1144555287546294e-016,
10, 8.1750892251729965, 9.1144560389833963e-016,
10, 8.7008367404341698, 5.3541847847332351e-016,
10, 8.7008367478847504, 5.3541844723127514e-016,
10, 9.1965094059705734, 3.3560389199894529e-016,
10, 9.196509413421154, 3.3560370421140035e-016,
10, 9.6678015515208244, 2.1059571242602905e-016,
10, 9.667801558971405, 2.1059579862533338e-016,
11, 0.72796426713466644, 2.3816013324880167e-006,
11, 0.72796427458524704, 2.3816003939892356e-006,
11, 1.5323273167014122, 2.6884539057733283e-008,
11, 1.5323273241519928, 2.6884532967611841e-008,
11, 2.3115825653076172, 6.7682186491374862e-010,
11, 2.3115825727581978, 6.768217495558193e-010,
11, 3.0679489448666573, 3.0544688315708309e-011,
11, 3.0679489523172379, 3.054468452617125e-011,
11, 3.8027990683913231, 2.186949692679853e-012,
11, 3.8027990758419037, 2.1869496552307528e-012,
11, 4.5169280469417572, 2.2947351040888336e-013,
11, 4.5169280543923378, 2.2947348773371414e-013,
11, 5.2107065990567207, 3.3353640359739479e-014,
11, 5.2107066065073013, 3.335363154144172e-014,
11, 5.8841593414545059, 6.4351159890193404e-015,
11, 5.8841593489050865, 6.435115249418266e-015,
11, 6.5370001494884491, 1.5930548620019077e-015,
11, 6.5370001569390297, 1.59305482454352e-015,
11, 7.1686412021517754, 4.9183710858836462e-016,
11, 7.168641209602356, 4.9183713116224844e-016,
11, 7.7781866490840912, 1.8464804238726119e-016,
11, 7.7781866565346718, 1.8464801709489452e-016,
11, 8.3644260391592979, 8.2263320775918765e-017,
11, 8.3644260466098785, 8.2263297787354186e-017,
11, 8.9258662387728691, 4.2375346461043962e-017,
11, 8.9258662462234497, 4.2375329267920249e-017,
11, 9.4609249979257584, 2.4482616150935944e-017,
11, 9.460925005376339, 2.4482619419420328e-017,
11, 9.968697115778923, 1.5254006021629585e-017,
11, 9.9686971232295036, 1.5254009931381535e-017,
11, 10.451380550861359, 9.7234265908062383e-018,
11, 10.451380558311939, 9.7234241296957717e-018,
11, 10.909898526966572, 6.3147967493977272e-018,
11, 10.909898534417152, 6.3147961332742783e-018,
12, 0.73438271880149841, 1.6566052109275983e-006,
12, 0.73438272625207901, 1.6566045155548952e-006,
12, 1.5445517003536224, 1.5860666784152459e-008,
12, 1.544551707804203, 1.5860658867525684e-008,
12, 2.33091701567173, 3.380059232629492e-010,
12, 2.3309170231223106, 3.3800585031437757e-010,
12, 3.095676377415657, 1.2884951580384992e-011,
12, 3.0956763848662376, 1.2884947453406877e-011,
12, 3.8402339816093445, 7.7768150022424858e-013,
12, 3.8402339890599251, 7.776814490345326e-013,
12, 4.5654622465372086, 6.8670097741098332e-014,
12, 4.5654622539877892, 6.8670083429297199e-014,
12, 5.2718541026115417, 8.38897478481124e-015,
12, 5.2718541100621223, 8.3889742372884196e-015,
12, 5.9596048966050148, 1.3595098759195538e-015,
12, 5.9596049040555954, 1.3595098086325987e-015,
12, 6.6286550611257553, 2.827608125932321e-016,
12, 6.6286550685763359, 2.8276074709416868e-016,
12, 7.2787091657519341, 7.3455425966950088e-017,
12, 7.2787091732025146, 7.3455407058460195e-017,
12, 7.9092400223016739, 2.3285371485286439e-017,
12, 7.9092400297522545, 2.3285373002934362e-017,
12, 8.5194846615195274, 8.8196897130872609e-018,
12, 8.519484668970108, 8.8196870510698361e-018,
12, 9.1084426045417786, 3.9108299199270105e-018,
12, 9.1084426119923592, 3.9108294386613317e-018,
12, 9.6749012768268585, 1.9869557945423399e-018,
12, 9.6749012842774391, 1.9869552330025425e-018,
12, 10.217559166252613, 1.1287442348688164e-018,
12, 10.217559173703194, 1.1287443889939456e-018,
12, 10.735452570021152, 6.9443548691210419e-019,
12, 10.735452577471733, 6.944357314519752e-019,
12, 11.229217991232872, 4.4666546191302752e-019,
12, 11.229217998683453, 4.4666540313320006e-019,
12, 11.70328252017498, 2.9048073087968165e-019,
12, 11.703282527625561, 2.9048072811110897e-019,
12, 12.068153090775013, 1.1197702118472231e-019,
12, 12.068153098225594, 1.1197723483874788e-019,
13, 0.74004656821489334, 1.1833107184182451e-006,
13, 0.74004657566547394, 1.1833097788321784e-006,
13, 1.5553053691983223, 9.7255030271643972e-009,
13, 1.5553053766489029, 9.7254989421162363e-009,
13, 2.3478806391358376, 1.7766237223855854e-010,
13, 2.3478806465864182, 1.7766232949140296e-010,
13, 3.1199393272399902, 5.7956070959333847e-012,
13, 3.1199393346905708, 5.7956067491263655e-012,
13, 3.8728964179754257, 2.9884476000191141e-013,
13, 3.8728964254260063, 2.9884474795319482e-013,
13, 4.6076695621013641, 2.2511499024927438e-014,
13, 4.6076695695519447, 2.2511492228003123e-014,
13, 5.3248289301991463, 2.3433299337790734e-015,
13, 5.3248289376497269, 2.3433296697120652e-015,
13, 6.024680033326149, 3.2334220967999432e-016,
13, 6.0246800407767296, 3.2334220842696106e-016,
13, 6.7073097303509712, 5.7246438539858037e-017,
13, 6.7073097378015518, 5.7246428548181273e-017,
13, 7.3726105690002441, 1.2665024613881524e-017,
13, 7.3726105764508247, 1.2665026479377931e-017,
13, 8.0202914103865623, 3.4243381685006812e-018,
13, 8.0202914178371429, 3.4243378550191225e-018,
13, 8.6498793810606003, 1.1097375058286432e-018,
13, 8.6498793885111809, 1.1097372565619852e-018,
13, 9.2607174664735794, 4.2345371873704953e-019,
13, 9.26071747392416, 4.234535720073341e-019,
13, 9.8519651964306831, 1.8698553775808502e-019,
13, 9.8519652038812637, 1.8698556036241362e-019,
13, 10.4226189032197, 9.3834845010114218e-020,
13, 10.42261891067028, 9.3834849389696859e-020,
13, 10.971594020724297, 5.2445253216776838e-020,
13, 10.971594028174877, 5.244524330346984e-020,
13, 11.497980736196041, 3.1824489086388546e-020,
13, 11.497980743646622, 3.1824504032698097e-020,
13, 12.001743614673615, 2.0418153275918044e-020,
13, 12.001743622124195, 2.0418144824511038e-020,
13, 12.485165499150753, 1.322118298528861e-020,
13, 12.485165506601334, 1.3221186083014843e-020,
13, 12.954543322324753, 8.948147552026443e-021,
13, 12.954543329775333, 8.9481497369716256e-021,
13, 13.141756847500801, 6.2261736884111559e-021,
13, 13.141756854951382, 6.22616137965588e-021,
14, 0.74509535729885101, 8.6479103419423106e-007,
14, 0.74509536474943161, 8.6479038219181041e-007,
14, 1.5648643523454666, 6.1650149037400714e-009,
14, 1.5648643597960472, 6.1650125500027851e-009,
14, 2.3629250749945641, 9.7577785849465913e-011,
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47, 16.819413423538208, 1.5939494724979777e-45,
47, 17.558761104941368, 1.3297370293876914e-46,
47, 17.558761112391949, 1.3297368751262161e-46,
47, 18.293107204139233, 1.2179710847308252e-47,
47, 18.293107211589813, 1.2179707147570524e-47,
47, 19.022494435310364, 1.2199964409560206e-48,
47, 19.022494442760944, 1.2199963161526724e-48,
47, 19.746959328651428, 1.3314974725625693e-49,
47, 19.746959336102009, 1.3314971821520744e-49,
47, 20.466532692313194, 1.5780264769522475e-50,
47, 20.466532699763775, 1.5780266017287087e-50,
47, 21.181239917874336, 2.0245178949773632e-51,
47, 21.181239925324917, 2.0245176315944763e-51,
47, 21.891101337969303, 2.8034948326469036e-52,
47, 21.891101345419884, 2.8034939685782499e-52,
47, 22.596132457256317, 4.1790008688926555e-53,
47, 22.596132464706898, 4.1790007819449501e-53,
47, 23.29634415358305, 6.688690844654884e-54,
47, 23.29634416103363, 6.6886903967838107e-54,
47, 23.99174290150404, 1.1467692853330373e-54,
47, 23.99174290895462, 1.146769186365129e-54,
47, 24.682330906391144, 2.1014056175149362e-55,
47, 24.682330913841724, 2.101405889230169e-55,
47, 25.368106223642826, 4.1070651602071193e-56,
47, 25.368106231093407, 4.1070645819933285e-56,
47, 26.04906290769577, 8.5443697879268007e-57,
47, 26.049062915146351, 8.5443710805019463e-57,
47, 26.725191041827202, 1.8886008815516421e-57,
47, 26.725191049277782, 1.8886004404999289e-57,
47, 27.396476879715919, 4.4272816933018068e-58,
47, 27.3964768871665, 4.4272808898189293e-58,
47, 28.062902852892876, 1.0988364859194886e-58,
47, 28.062902860343456, 1.0988366253920036e-58,
47, 28.724447630345821, 2.8828564728611722e-59,
47, 28.724447637796402, 2.8828560006070042e-59,
47, 29.381086193025112, 7.9823493818795601e-60,
47, 29.381086200475693, 7.9823486515529818e-60,
47, 30.032789826393127, 2.3291962093493194e-60,
47, 30.032789833843708, 2.3291961095189567e-60,
47, 30.679526172578335, 7.1518997842789854e-61,
47, 30.679526180028915, 7.1518973239508013e-61,
47, 31.321259275078773, 2.307641249370635e-61,
47, 31.321259282529354, 2.3076405172220076e-61,
47, 31.957949593663216, 7.8136659014415755e-62,
47, 31.957949601113796, 7.8136652248427256e-62,
47, 32.58955405652523, 2.7726990903841993e-62,
47, 32.589554063975811, 2.7726984868935579e-62,
47, 33.216026142239571, 1.0297785058851998e-62,
47, 33.216026149690151, 1.0297785317772547e-62,
47, 33.837315931916237, 3.9977694884441333e-63,
47, 33.837315939366817, 3.9977697411365166e-63,
47, 34.453370243310928, 1.6201996067502547e-63,
47, 34.453370250761509, 1.6201992966533348e-63,
47, 35.064132787287235, 6.8460691000177028e-64,
47, 35.064132794737816, 6.8460695120204543e-64,
47, 35.669544346630573, 3.0121713471962354e-64,
47, 35.669544354081154, 3.0121713531964038e-64,
47, 36.269543081521988, 1.3784224526611186e-64,
47, 36.269543088972569, 1.3784225213845312e-64,
47, 36.864064872264862, 6.551031389687536e-65,
47, 36.864064879715443, 6.5510309622836689e-65,
47, 37.453043811023235, 3.2290009692268078e-65,
47, 37.453043818473816, 3.2290017963920392e-65,
47, 38.036412797868252, 1.6483961550238261e-65,
47, 38.036412805318832, 1.6483964581988733e-65,
47, 38.614104360342026, 8.7068430193326876e-66,
47, 38.614104367792606, 8.7068402136754839e-66,
47, 39.186051674187183, 4.7496069713042888e-66,
47, 39.186051681637764, 4.7496053262899038e-66,
47, 39.752189837396145, 2.6711638237868345e-66,
47, 39.752189844846725, 2.6711631630388347e-66,
47, 40.312457516789436, 1.5462743542785204e-66,
47, 40.312457524240017, 1.5462749942360846e-66,
47, 40.86679895222187, 9.197576438140017e-67,
47, 40.866798959672451, 9.1975795377859577e-67,
47, 41.415166474878788, 5.6155851602094119e-67,
47, 41.415166482329369, 5.6155838713502694e-67,
47, 41.957523517310619, 3.5168645107743576e-67,
47, 41.9575235247612, 3.5168655127769924e-67,
47, 42.493848152458668, 2.2493284118817205e-67,
47, 42.493848159909248, 2.2493291438227212e-67,
47, 43.024137258529663, 1.4660650820917181e-67,
47, 43.024137265980244, 1.4660656316867027e-67,
47, 43.548411078751087, 9.7156719582995046e-68,
47, 43.548411086201668, 9.7156694474500461e-68,
47, 44.066718079149723, 6.5310259797325952e-68,
47, 44.066718086600304, 6.5310260879476081e-68,
47, 44.579139620065689, 4.442438020547419e-68,
47, 44.57913962751627, 4.4424398622724323e-68,
47, 45.085793986916542, 3.0499884991862987e-68,
47, 45.085793994367123, 3.0499874345652321e-68,
47, 45.586839035153389, 2.1083600642969428e-68,
47, 45.58683904260397, 2.1083609447567937e-68,
47, 46.082472607493401, 1.4938147144274907e-68,
47, 46.082472614943981, 1.4938153354270103e-68,
47, 46.572930358350277, 1.0602813294127947e-68,
47, 46.572930365800858, 1.0602819311490823e-68,
47, 47.058480642735958, 7.5239566886279539e-69,
47, 47.058480650186539, 7.5239599111949498e-69,
47, 47.539416998624802, 5.3260856872391961e-69,
47, 47.539417006075382, 5.3260856592438819e-69,
48, 0.8081108033657074, 3.790537216779154e-09,
48, 0.80811081081628799, 3.7905242694972794e-09,
48, 1.6817938983440399, 2.2504531682948115e-12,
48, 1.6817939057946205, 2.2504483820670804e-12,
48, 2.5442163795232773, 2.9637275510465843e-15,
48, 2.5442163869738579, 2.9637227676420457e-15,
48, 3.3967880085110664, 6.9290264628280876e-18,
48, 3.396788015961647, 6.9290183337540871e-18,
48, 4.2404954805970192, 2.5428118362391363e-20,
48, 4.2404954880475998, 2.5428091511397717e-20,
48, 5.0760592594742775, 1.3535154982948461e-22,
48, 5.0760592669248581, 1.3535145215948875e-22,
48, 5.9040295109152794, 9.8885231815640295e-25,
48, 5.90402951836586, 9.8885168237279911e-25,
48, 6.7248413637280464, 9.518302995514812e-27,
48, 6.724841371178627, 9.5183002220780383e-27,
48, 7.5388482883572578, 1.1696457368664985e-28,
48, 7.5388482958078384, 1.169645374012626e-28,
48, 8.346343457698822, 1.7894833131442889e-30,
48, 8.3463434651494026, 1.7894824081088118e-30,
48, 9.1475740000605583, 3.3397293242857729e-32,
48, 9.1475740075111389, 3.339728176140613e-32,
48, 9.9427508860826492, 7.4755270996096026e-34,
48, 9.9427508935332298, 7.4755256245678417e-34,
48, 10.732056066393852, 1.9783655047619916e-35,
48, 10.732056073844433, 1.9783652198251722e-35,
48, 11.51564771682024, 6.1149507579983915e-37,
48, 11.515647724270821, 6.1149494622707338e-37,
48, 12.293664202094078, 2.1842708433101863e-38,
48, 12.293664209544659, 2.1842700354917262e-38,
48, 13.066227130591869, 8.9337538377953074e-40,
48, 13.06622713804245, 8.9337502859897068e-40,
48, 13.833443738520145, 4.149924620485562e-41,
48, 13.833443745970726, 4.1499237221656357e-41,
48, 14.595408782362938, 2.1736394745132589e-42,
48, 14.595408789813519, 2.173638813907354e-42,
48, 15.352206096053123, 1.2754745467294774e-43,
48, 15.352206103503704, 1.2754741693102798e-43,
48, 16.103909812867641, 8.3362836739294546e-45,
48, 16.103909820318222, 8.3362820544674689e-45,
48, 16.850585393607616, 6.0368656344880979e-46,
48, 16.850585401058197, 6.0368647910276049e-46,
48, 17.592290475964546, 4.8208082646747087e-47,
48, 17.592290483415127, 4.8208065336171195e-47,
48, 18.32907559722662, 4.2267802866741478e-48,
48, 18.3290756046772, 4.2267788247523572e-48,
48, 19.060984767973423, 4.0527677000262975e-49,
48, 19.060984775424004, 4.0527678696098182e-49,
48, 19.788055963814259, 4.2340523931993972e-50,
48, 19.788055971264839, 4.2340524276091812e-50,
48, 20.510321587324142, 4.8034783455880191e-51,
48, 20.510321594774723, 4.8034780348731884e-51,
48, 21.227808803319931, 5.899186088148869e-52,
48, 21.227808810770512, 5.8991860762611689e-52,
48, 21.940539844334126, 7.8199420923371399e-53,
48, 21.940539851784706, 7.8199427514005375e-53,
48, 22.648532271385193, 1.1158743726808565e-53,
48, 22.648532278835773, 1.1158741101131104e-53,
48, 23.351799219846725, 1.7097382876171394e-54,
48, 23.351799227297306, 1.7097378990809491e-54,
48, 24.050349555909634, 2.8062000362026087e-55,
48, 24.050349563360214, 2.8062001977088843e-55,
48, 24.744188040494919, 4.9228561141945363e-56,
48, 24.744188047945499, 4.9228560221850344e-56,
48, 25.433315485715866, 9.2112000380037501e-57,
48, 25.433315493166447, 9.2111973817050768e-57,
48, 26.117728859186172, 1.8346687861945116e-57,
48, 26.117728866636753, 1.8346682350681618e-57,
48, 26.797421365976334, 3.8826606576023508e-58,
48, 26.797421373426914, 3.8826600315338496e-58,
48, 27.47238253057003, 8.7148703279250707e-59,
48, 27.472382538020611, 8.7148674108098657e-59,
48, 28.142598286271095, 2.0711829791398647e-59,
48, 28.142598293721676, 2.0711832322860257e-59,
48, 28.808050975203514, 5.203584010105859e-60,
48, 28.808050982654095, 5.2035828616190226e-60,
48, 29.468719460070133, 1.3798854862844653e-60,
48, 29.468719467520714, 1.3798850951568444e-60,
48, 30.124579109251499, 3.8565307240880389e-61,
48, 30.12457911670208, 3.8565299743759719e-61,
48, 30.775601848959923, 1.134342736876812e-61,
48, 30.775601856410503, 1.1343429076941469e-61,
48, 31.42175617814064, 3.5066060548219019e-62,
48, 31.421756185591221, 3.5066056252289294e-62,
48, 32.063007235527039, 1.1377415805787006e-62,
48, 32.063007242977619, 1.1377415420726448e-62,
48, 32.699316792190075, 3.8694437991207317e-63,
48, 32.699316799640656, 3.8694430678757391e-63,
48, 33.330643333494663, 1.3776788233453704e-63,
48, 33.330643340945244, 1.3776791661638305e-63,
48, 33.956942074000835, 5.1285952783952753e-64,
48, 33.956942081451416, 5.1285947602355625e-64,
48, 34.578165091574192, 1.9937127927134763e-64,
48, 34.578165099024773, 1.9937126593845751e-64,
48, 35.194261386990547, 8.0836531292607215e-65,
48, 35.194261394441128, 8.0836543290621667e-65,
48, 35.805177040398121, 3.4143034915050112e-65,
48, 35.805177047848701, 3.4143040521491458e-65,
48, 36.410855405032635, 1.5004193222265229e-65,
48, 36.410855412483215, 1.5004189889736636e-65,
48, 37.011237367987633, 6.8526723104762059e-66,
48, 37.011237375438213, 6.8526715926507402e-66,
};

//
// A bunch of helper functions used for calculating the coefficients:
//
boost::math::ntl::RR factorial(unsigned n)
{
   static boost::array<boost::array<boost::math::ntl::RR, 1>, 201> result; 
   static bool init = false;
   if(!init)
   {
      unsigned k = 1;
      boost::math::ntl::RR fact = 1;
      do{
         result[k-1][0] = fact;
         fact *= k++;
      }
      while(k < result.size());
      init = true;
   }
   //static const int array_size = result.size();
   if(n < result.size())
      return result[n][0];

   unsigned i = (unsigned)result.size()-1;
   boost::math::ntl::RR r = result[i][0];
   while(i < n)
      r *= ++i;
   return r;
}

template <class T>
T binomial(int n, int k, T)
{
   T result;
   if(k < 0) 
   {
      result = 0;
   }
   else if(k > n)
   {
      result = 1;
   }
   else
   {
      result = factorial(n);
      result /= factorial(k);
      result /= factorial(n-k);
   }
   return result;
}
//
// Functions for creating the matrices that generate the coefficents.
// See http://my.fit.edu/~gabdo/gamma.txt and http://www.rskey.org/gamma.htm
//
template <class T>
matrix<T> make_B(unsigned n, T)
{
   matrix<T> result(n, n);
   for(unsigned i = 0; i < n; ++i)
   {
      for(unsigned j = 0; j < n; ++j)
      {
         if(i == 0)
            result(i, j) = 1;
         else if(j >= i)
         {
            T r = binomial(i+j-1, j-i, T());
            if((j-i) %2)
               r = -r;
            result(i, j) = r;
         }
         else
            result(i, j) = 0;
      }
   }
   return result;
}

template <class T>
matrix<T> make_C(unsigned n, T)
{
   matrix<T> result(n, n);
   for(unsigned i = 0; i < n; ++i)
   {
      for(unsigned j = 0; j < n; ++j)
      {
         if((i==0) && (j == 0))
            result(i, j) = 0.5;
         else if(j > i)
            result(i, j) = 0;
         else
         {
            T r = 0;
            for(unsigned k = 0; k <= i; ++k)
            {
               r += binomial(2*i, 2*k, T()) * binomial(k, k+j-i, T());
            }
            if((i-j)%2)
               r = -r;
            result(i, j) = r;
         }
      }
   }
   return result;
}

template <class T>
matrix<T> make_D(unsigned n, T)
{
   matrix<T> result(n, n);
   for(unsigned i = 0; i < n; ++i)
   {
      for(unsigned j = 0; j < n; ++j)
      {
         if(i != j)
            result(i, j) = 0;
         else if(i == 0)
            result(i, j) = 1;
         else if(i == 1)
            result(i, j) = -1;
         else
         {
            T r = result(i-1, i-1) * (2 * (2*i - 1));
            r /= (i-1);
            result(i, j) = r;
         }
      }
   }
   return result;
}

template <class T>
matrix<T> make_F(unsigned n, T g)
{
   using namespace std;
   matrix<T> result(n, 1);
   for(unsigned i = 0; i < n; ++i)
   {
      T r = factorial(2 * i); 
      r /= factorial(i);
      r *= exp(double(i) + g + 0.5);
      r /= ldexp(1.0, (2*i)-1);
      r /= pow(g + i + 0.5, (long)i);
      r /= sqrt(g + i + 0.5);
      result(i, 0) = r;
   }
   return result;
}

template <class T>
struct lanczos_info
{
   int n;                   // number of coefficients
   T r;                     // the arbitrary parameter
   std::vector<T> c;        // the coefficients
   T err;                   // error found
};
//
// Create the coefficients, caching previous matrix values as appropriate:
//
template <class T>
lanczos_info<T> generate_lanczos(unsigned n, T g)
{
   static unsigned last_n = 0;
   static matrix<T> last_prefix;

   matrix<T> B, C, D;
   matrix<T> F, P, E, T1, T2;

   if(n == last_n)
   {
      F = make_F(n, g);
      P = prod(last_prefix, F);
   }
   else
   {
      B = make_B(n, g);
      C = make_C(n, g);
      D = make_D(n, g);
      F = make_F(n, g);

      T1 = prod(D, B);
      T2 = prod(T1, C);
      P = prod(T2, F);

      last_n = n;
      last_prefix = T2;
   }

   lanczos_info<T> result;
   result.n = n;
   result.r = g;
   for(unsigned i = 0; i < n; ++i)
      result.c.push_back(P(i, 0));

   return result;
}
//
// Returns the factorials and half factorials:
//
template <class T>
std::vector<std::vector<T> > const & get_test_data()
{
   // returns factorials and half factorials in the range [0, 100]:
   static std::vector<std::vector<T> > data;
   if(data.empty())
   {
      T fact = 1;
      int k = 1;
      std::vector<T> item;
      do
      {
         data.push_back(item);
         data.back().push_back(k-1);
         data.back().push_back(fact);
         data.back().push_back(log(fact));
         fact = fact * T(k++);
      }while(k < 100);

      fact = 0.5;
      T srpi = sqrt(boost::math::constants::pi<T>());
      T mul = 1.5;
      do{
         data.push_back(item);
         data.back().push_back(mul-1);
         data.back().push_back(fact*srpi);
         data.back().push_back(log(fact*srpi));
         fact *= mul;
         mul += 1;
      }while(mul < 100);
   }

   return data;
}

struct sort_on_0
{
   template<class T>
   bool operator ()(const T& a, const T& b)
   {
      return ((a[0]) < (b[0]));
   }
};
//
// Get test data for points near 0, 1, 2 etc.
template <class T>
std::vector<std::vector<T> > const & get_test_data_near_x(T x)
{
   // 
   // this *must* be called with a *very* high precision
   // type T in order to stand any chance of long double
   // precision results:
   //
   using namespace std;
   using namespace boost::math;
   static std::vector<std::vector<T> > data;
   static T last_x(-10000);
   if(last_x != x)
      data.clear();
   if(!data.size())
   {
      last_x = x;
      std::vector<T> item;
      for(int i = -4; i > -20; --i)
      {
         double val = ldexp(2.0, i);
         data.push_back(item);
         data.back().push_back(val+x);
         data.back().push_back(tgamma(T(val)+x));
         data.back().push_back(lgamma(T(val)+x));
         data.push_back(item);
         data.back().push_back(-val+x);
         data.back().push_back(tgamma(T(-val)+x));
         data.back().push_back(lgamma(T(-val)+x));
      }
      T v = x-0.5;
      T interval = T(1)/8;
      while(v <= x+0.5)
      {
         if((floor(v) != v) || (v > 0))
         {
            data.push_back(item);
            data.back().push_back(v);
            data.back().push_back(tgamma(v));
            data.back().push_back(lgamma(v));
         }
         v += interval;
      }
      //
      // sort data:
      //
      std::sort(data.begin(), data.end(), sort_on_0());
   }
   return data;
}

template <class T>
std::vector<std::vector<T> > const & get_test_data_near_1()
{
   return get_test_data_near_x(T(1));
}

template <class T>
std::vector<std::vector<T> > const & get_test_data_near_2()
{
   return get_test_data_near_x(T(2));
}

//
// Converts Lanczos approximation into rational form via 
// polynomial arithmetic:
//
template <class T>
struct lanczos_rational
{
   std::vector<T> num, denom;
   int N;
   T g;

   T gamma(T z)
   {
      using namespace std;
      BOOST_ASSERT(num.size() == denom.size());
      BOOST_ASSERT(num.size() == N);
      T zgh = z + g - T(0.5);
      T prefix = pow(zgh, T(z - 0.5)) / exp(zgh);
      T s1, s2;
      if(z < 1)
      {
         s1 = num[N-1];
         s2 = denom[N-1];
         for(unsigned i = N-2; i >= 0; --i)
         {
            s1 *= z;
            s2 *= z;
            s1 += num[i];
            s2 += num[i];
         }
         s1 /= s2;
         return prefix * s1;
      }
      else
      {
         z = 1/z;
         s1 = num[0];
         s2 = denom[0];
         for(int i = 1; i < N; ++i)
         {
            s1 *= z;
            s2 *= z;
            s1 += num[i];
            s2 += denom[i];
         }
         s1 /= s2;
         return prefix * s1;
      }
   }
   T factorial(T z)
   {
      return this->gamma(z+1);
   }
   template <class U>
   lanczos_rational(const lanczos_info<U>& info)
   {
      U l_denom_coef[2] = { 0, 1 };
      boost::math::tools::polynomial<U> l_denom(l_denom_coef, 1), l_num(info.c[1]);
      for(unsigned i = 2; i < info.c.size(); ++i)
      {
         l_denom_coef[0] = i - 1;
         boost::math::tools::polynomial<U> bot(l_denom_coef, 1), top(info.c[i]);
         l_num *= bot;
         top *= l_denom;
         l_denom *= bot;
         l_num += top;
      }
      l_num += l_denom * info.c[0];
      for(unsigned i = 0; i < info.c.size(); ++i)
      {
         num.push_back(boost::math::tools::real_cast<T>(l_num[i]));
         denom.push_back(boost::math::tools::real_cast<T>(l_denom[i]));
      }
      //std::cout << l_num << std::endl;
      //std::cout << l_denom << std::endl;
      BOOST_ASSERT(num.size() == l_num.degree()+1);
      BOOST_ASSERT(denom.size() == l_denom.degree()+1);
      g = boost::math::tools::real_cast<T>(info.r);
      N = info.n;
      /*
      for(unsigned i = 0; i < num.size(); ++i)
         std::cout << num[i] << " ";
      std::cout << std::endl;
      for(unsigned i = 0; i < denom.size(); ++i)
         std::cout << denom[i] << " ";
      std::cout << std::endl;
      */
   }
};
//
// Code to test an approximation against the factorials and
// half factorials, returns the max error found:
//
template <class T, class R>
T get_max_error(const lanczos_info<T>& dat, R)
{
   T max_error = 0;
   std::vector<std::vector<T> > const & tests = get_test_data<T>();
   lanczos_rational<R> rational(dat);

   for(unsigned i = 0; i < tests.size(); ++i)
   {
      R input = boost::math::tools::real_cast<R>(tests[i][0]);
      if(std::numeric_limits<R>::is_specialized && (boost::math::tools::real_cast<R>(tests[i][1]) > (std::numeric_limits<R>::max)()))
         continue;

      R gamr = rational.factorial(input);
      if(std::numeric_limits<R>::is_specialized && (gamr > (std::numeric_limits<R>::max)()))
         continue;
      T gam = gamr;
      T exp = tests[i][1];
      T err = boost::math::tools::relative_error(gam, exp);
      if(err > max_error)
         max_error = err;
   }

   return max_error;
}
//
// This is what prints the "best" approximation out as code:
//
template <class T>
void print_code(const lanczos_info<T>& l, const char* name)
{
   using namespace std;
   lanczos_info<T> l2(l);
   T factor = exp(l.r);
   for(unsigned k = 0; k < l2.c.size(); ++k)
      l2.c[k] /= factor;

   lanczos_rational<T> rat(l);
   T max_term = 0;
   for(unsigned i = 0; i < rat.denom.size(); ++i)
   {
      if(rat.denom[i] > max_term)
         max_term = rat.denom[i];
   }
   const char* denom_type;
   const char* cast_type;
   const char* suffix_type;
   if(max_term < (std::numeric_limits<boost::uint16_t>::max)())
   {
      denom_type = "boost::uint16_t";
      cast_type = "static_cast<boost::uint16_t>";
      suffix_type = "u";
   }
   else if(max_term < (std::numeric_limits<boost::uint32_t>::max)())
   {
      denom_type = "boost::uint32_t";
      cast_type = "static_cast<boost::uint32_t>";
      suffix_type = "u";
   }
#ifdef BOOST_HAS_LONG_LONG
   else if(max_term < (std::numeric_limits<boost::uint64_t>::max)())
   {
      denom_type = "boost::uint64_t";
      cast_type = "";
      suffix_type = "uLL";
   }
#endif
   else
   {
      denom_type = "T";
      cast_type = "static_cast<T>";
      suffix_type = "L";
   }
   
   std::cout <<
      "//\n"
      "// Lanczos Coefficients for N=" << l.n << " G=" << l.r << "\n"
      "// Max experimental error (with ";
   if(std::strlen(name) == 0)
      std::cout << "arbitary";
   else
      std::cout << name;
   std::cout << " precision arithmetic) " << l.err <<
      "\n// Generated with compiler: " << BOOST_COMPILER << " on " << BOOST_PLATFORM << " at " << __DATE__ << "\n"
      "//\n"
      "struct lanczos" << l.n << name << "\n"
      "{\n"
      "   template <class T>\n"
      "   static T lanczos_sum(const T& z)\n"
      "   {\n"
      "      static const T num[" << rat.num.size() << "] = {\n";

   for(unsigned i = 0; i < rat.num.size(); ++i)
   {
      std::cout << "         static_cast<T>(" << rat.num[i] << "L)";
      if(i != rat.num.size() - 1)
         std::cout << ",";
      std::cout << "\n";
   }
   std::cout << 
      "      };\n"
      "      static const " << denom_type << " denom[" << rat.denom.size() << "] = {\n";
   for(unsigned i = 0; i < rat.denom.size(); ++i)
   {
      std::cout << "         " << cast_type << "(" << rat.denom[i] << suffix_type << ")";
      if(i != rat.denom.size() - 1)
         std::cout << ",";
      std::cout << "\n";
   }
   std::cout << 
      "      };\n"
      "      return boost::math::tools::evaluate_rational(num, denom, z);\n"
      "   }\n\n"
      "   template <class T>\n"
      "   static T lanczos_sum_expG_scaled(const T& z)\n"
      "   {\n"
      "      static const T num[" << rat.num.size() << "] = {\n";

   for(unsigned i = 0; i < rat.num.size(); ++i)
   {
      std::cout << "         static_cast<T>(" << (rat.num[i]/factor) << "L)";
      if(i != rat.num.size() - 1)
         std::cout << ",";
      std::cout << "\n";
   }
   std::cout << 
      "      };\n"
      "      static const " << denom_type << " denom[" << rat.denom.size() << "] = {\n";
   for(unsigned i = 0; i < rat.denom.size(); ++i)
   {
      std::cout << "         " << cast_type << "(" << rat.denom[i] << suffix_type << ")";
      if(i != rat.denom.size() - 1)
         std::cout << ",";
      std::cout << "\n";
   }
   std::cout << 
      "      };\n"
      "      return boost::math::tools::evaluate_rational(num, denom, z);\n"
      "   }\n\n";

   std::cout <<
      "\n   template<class T>\n"
      "   static T lanczos_sum_near_1(const T& dz)\n"
      "   {\n"
      "      static const T d[" << l2.n-1 << "] = {\n";

   factor = sqrt((l.r + 0.5)/boost::math::constants::e<T>()) / exp(l.r);

   for(int i = 1; i < l.n; ++i)
   {
      std::cout << "         static_cast<T>(" << l.c[i]*factor << "L)";
      if(i != l.n - 1)
         std::cout << ",";
      std::cout << "\n";
   }
   std::cout << 
      "      };\n"
      "      T result = 0;\n"
      "      for(unsigned k = 1; k <= sizeof(d)/sizeof(d[0]); ++k)\n"
      "      {\n"
      "         result += (-d[k-1]*dz)/(k*dz + k*k);\n"
      "      }\n"
      "      return result;\n"
      "   }\n";

   std::cout <<
      "\n   template<class T>\n"
      "   static T lanczos_sum_near_2(const T& dz)\n"
      "   {\n"
      "      static const T d[" << l2.n-1 << "] = {\n";

   factor = pow(boost::math::constants::e<T>()/(l.r+1.5), T(1.5)) * exp(l.r);
      //pow((l.r + 1.5)/boost::math::constants::e<T>(), 1.5) / exp(l.r);

   for(int i = 1; i < l.n; ++i)
   {
      std::cout << "         static_cast<T>(" << l.c[i]/factor << "L),\n";
   }
   std::cout << 
      "      };\n"
      "      T result = 0;\n"
      "      T z = dz + 2;\n"
      "      for(unsigned k = 1; k <= sizeof(d)/sizeof(d[0]); ++k)\n"
      "      {\n"
      "         result += (-d[k-1]*dz)/(z + k*z + k*k - 1);\n"
      "      }\n"
      "      return result;\n"
      "   }\n\n"
      "   static double g(){ return " << l.r << "; }\n"
      "};\n\n";
}
//
// Print out the test values:
//
void print_test_values(const std::vector<std::vector<boost::math::ntl::RR> >& v, const char* name, int offset = 1)
{
   std::cout << "#define SC_(x) static_cast<T>(BOOST_JOIN(x, L))\n";
   std::cout << 
      "   static const boost::array<boost::array<T, 3>, " << v.size() << "> " << name << " = {\n";
   for(unsigned i = 0; i < v.size(); ++i)
   {
      std::cout << "      SC_(" << (v[i][0] + offset) << "), SC_(" << v[i][1] << "), SC_(" << v[i][2] << "),\n";
   }
   std::cout << "   };\n#undef SC_\n\n";
}
//
// Get the error for a specific approximation, and print out it's code:
//
void calculate_lanczos_spot(int n, boost::math::ntl::RR r, const char* suffix = "")
{
   lanczos_info<boost::math::ntl::RR> info = generate_lanczos(n, r);
   // note error is calculated at high precision:
   info.err = get_max_error(info, r);
   print_code(info, suffix);
}
//
// Scan the sweet-spots for the best approximation:
//
template <class T>
void find_best_lanczos(const char* name, T eps, int max_scan = 100)
{
   using namespace std;

   lanczos_info<boost::math::ntl::RR> best;
   best.err = 100; // best case had better be better than this!
   for(int i = 0; i < sizeof(sweet_spots)/sizeof(sweet_spots[0]); ++i)
   {
      if((sweet_spots[i].err < eps*10) && (sweet_spots[i].N < max_scan))
      {
         lanczos_info<boost::math::ntl::RR> info = generate_lanczos(sweet_spots[i].N, boost::math::ntl::RR(sweet_spots[i].g));
         boost::math::ntl::RR err = get_max_error(info, eps);
         if(err/eps < 1000)
         {
            std::cout << sweet_spots[i].N << " " << sweet_spots[i].g << " " << err/eps << std::endl;
         }
         if(err < best.err)
         {
            best = info;
            best.err = err;
         }
      }
   }
   std::cout << std::endl;

   print_code(best, name);
}
int test_main(int argc, char*argv [])
{
   bool test_double(false), test_long(false), test_float(false), test_quad(false), spots(false), test_data(false);

   if(argc < 2)
   {
      std::cout << 
         "Useage:\n"
         "  -float        test type float for the best approximation\n"
         "  -double       test type double for the best approximation\n"
         "  -long-double  test type long double for the best approximation\n"
         "  -spots        print out the best cases found in previous runs\n"
         "  -data         print out the test data\n" << std::flush;
      return 0;
   }

   for(int i = 1; i < argc; ++i)
   {
      if(std::strcmp(argv[i], "-float") == 0)
         test_float = true;
      else if(std::strcmp(argv[i], "-double") == 0)
         test_double = true;
      else if(std::strcmp(argv[i], "-long-double") == 0)
         test_long = true;
      else if(std::strcmp(argv[i], "-quad-float") == 0)
         test_quad = true;
      else if(std::strcmp(argv[i], "-spots") == 0)
         spots = true;
      else if(std::strcmp(argv[i], "-data") == 0)
         test_data = true;
   }

   boost::math::ntl::RR::SetPrecision(1000);
   boost::math::ntl::RR::SetOutputPrecision(40);

   if(spots)
   {
      // these are optimal N and R from Pugh:
      calculate_lanczos_spot(61, 63.192152);
      calculate_lanczos_spot(31, 32.080670);
      calculate_lanczos_spot(6, 5.581);
      calculate_lanczos_spot(11, 10.900511);
      calculate_lanczos_spot(13, 13.144565);
      calculate_lanczos_spot(22, 22.61891);
      // these are the best cases we've already determined
      // from previous runs:
      calculate_lanczos_spot(6, 1.428456135094165802001953125);
      calculate_lanczos_spot(13, 6.024680040776729583740234375);
      calculate_lanczos_spot(17, 12.2252227365970611572265625);
      boost::math::ntl::RR::SetOutputPrecision(90);
      calculate_lanczos_spot(24, 20.3209821879863739013671875);
   }
   if(test_float)
   {
      find_best_lanczos("float", std::numeric_limits<float>::epsilon());
   }
   if(test_double)
   {
      find_best_lanczos("double", std::numeric_limits<double>::epsilon());
   }

   if(test_long)
   {
      find_best_lanczos("long_double", std::numeric_limits<long double>::epsilon());
   }
   if(test_quad)
   {
      //find_best_lanczos("quad_float", pow(NTL::quad_float(2), NTL::quad_float(-105)));
   }
   if(test_data)
   {
      std::cout << "Test Data follows:\n\n";

      boost::math::ntl::RR::SetPrecision(1000);
      
      std::vector<std::vector<boost::math::ntl::RR> > const & tests = get_test_data<boost::math::ntl::RR>();
      print_test_values(tests, "factorials");
      print_test_values(get_test_data_near_1<boost::math::ntl::RR>(), "near_1", 0);
      print_test_values(get_test_data_near_2<boost::math::ntl::RR>(), "near_2", 0);
      print_test_values(get_test_data_near_x<boost::math::ntl::RR>(boost::math::ntl::RR(0)), "near_0", 0);
      print_test_values(get_test_data_near_x<boost::math::ntl::RR>(boost::math::ntl::RR(-10)), "near_m10", 0);
      print_test_values(get_test_data_near_x<boost::math::ntl::RR>(boost::math::ntl::RR(-55)), "near_m55", 0);
   }
   return 0;
}