update coverage

Author: heltonmc

src/U_polynomials.jl

@@ -56,8 +56,8 @@ besseljy_debye_fit(x::Float64) = 2.0 + 1.00035*x + Base.Math._approx_cbrt(302.68
 besseljy_debye_cutoff(nu, x::Float64) = nu > besseljy_debye_fit(x) && nu > 15
 
 # Float128 - provide roughly ~1e-35 precision
-besseljy_debye_fit(x) = 16.0 + 1.0012*x + Base.Math._approx_cbrt(Float64(27.91*x))
-besseljy_debye_cutoff(nu, x) = nu > besseljy_debye_fit(x) && nu > 40
+#besseljy_debye_fit(x) = 16.0 + 1.0012*x + Base.Math._approx_cbrt(Float64(27.91*x))
+#besseljy_debye_cutoff(nu, x) = nu > besseljy_debye_fit(x) && nu > 40
 
 #####
 ##### Debye large order expansion coefficients
@@ -214,8 +214,8 @@ function Uk_poly_Jn(p, v, p2, x::T) where T
     u18 = evalpoly(p2, (17402648254366970318896442155853313710334325579833984375, -7038996381042630440706320955207218277836259136214144531250, 491515845627995608630308828025937223574193727753376100859375, -14053430579296992819276840860026711644143962037898462198750000, 217977088167107844476671248240850549184342689357081793983627500, -2108435312318793739666233505677933977608124223061200130648299000, 13796125542556918366536045481173150324297945459901440053845493532, -64294535084989436534160300189785402934009324116808204634140090960, 220746706223415685134843293656748091845034254294736425040548604450, -570806748959722117700578156666367879819767325936645047354739587500, 1126546324172034608288726239622146620694308510621969350884171781250, -1707362295067866555400074337741150183369276187369879432556935250000, 1985632470023144029098600377248581775333320983961309350778555937500, -1757491631661175749558620292180560395062012011157079033996696875000, 1163006497421870129179321717633624986894646606476886508372367187500, -557267390747417776045984733477369082240579802825315540376718750000, 182639522233726398821209338567430722027981720466294556325849609375, -36632957438678377189819992330283878474735514770655121685644531250, 3391940503581331221279628919470729488401436552838437193115234375)) / 40857116649582343206344405082690331607040000000
     u19 = evalpoly(p2, (3761719809509744584195141470215239968860161850335693359375, -1694136104847790923543013061207680485991618397125797873046875, 131518243789528012257323287684549590712219590887856743595703125, -4179129511260217209133623104486559148268490002722848244991240625, 72088266652871136165181276891337618088740053757385007292240377500, -776773977214820033621339638022401758862209813648991378355261599500, 5677394779818071523358076045292335690018332546439714641415150037636, -29667822591847140970922062603154078948366431649274897672067206014580, 114800233558787974267088388638654159779991991716667850063043161177890, -336788945225975997668966486515468748933754942064781256509404101275850, 760599837839891632010677514872412397427959022966733699428711208643750, -1333776105497652608917805362422659621440699261908512326717363285968750, 1821026790520428617034899967974569000414933301802005578083458082187500, -1929493390007550512825649545883182667978851358936168551128507440937500, 1570570304135828069768211937927131121377451398878663909061910457812500, -963355744456233323886326422779275308259891167457840027230510476562500, 430744376085805585076333464506126217301082462279781595185335615234375, -132496442776420617057548516091451843431549350710841121872822607421875, 25067118709566753991500713640672585065184296412786618544560205078125, -2198870062242697718552694179006367110981078632700580574084228515625)) / 980570799589976236952265721984567958568960000000
     u20 = evalpoly(p2, (24030618487110150352755402740028995969072485932314476318359375, -11984379509393886990049682627416290126645799829432886859195312500, 1028816773596376675865176501621547688509187479681533744365175781250, -36136687211104276266628386141898079997731413843884113675698994212500, 689368394712060288513879526213143279387995331221181709158682715671875, -8226054591925395046897310265711439061232478740113589342810359827971600, 66729727980314206345594148553023187689855688854650386958448499394886808, -388235990422199036481157075090292917209702371811112714609151030385239376, 1679621555358289731717827244171539003686077073065087747585589973854567950, -5539024239213671573375139503069183935283937579486175606155069574939719000, 14158993985687610069291256086138044030538819617545349322200892501730615500, -28351273454978996507857547213496220444080209057761228571819950144231575000, 44700502703654649774362294361156754154712937287563245669685024068473468750, -55504285315413734114196925709059066734450069228526494661323483726671250000, 53996017865021964397812742861916826936440170527545458081631955240159375000, -40677946447845849750014263157052100427921007881029648608310681741346250000, 23250401373415767366970579801426233116241475047289980901600427561701171875, -9744288621182737996606001891415854305391392962559389056448157541132812500, 2823768330714179467082676027577350697536971031741905897356560592675781250, -505537818270094147077012813306995849751437826286925078626556809570312500, 42128151522507845589751067775582987479286485523910423218879734130859375)) / 658943577324464031231922565173629668158341120000000
-       
-    Poly = (u0, u1, u2, u3, u4, u5, u6, u7, u8, u9, u10, u11, u12, u13, u14, u15, u16,  u17, u18, u19, u20)
+    u21 = evalpoly(p2, (1990919576929585163761247434580873723897677550573731281207275390625, -1094071724893410272201314846735800345180930752601602224440504638671875, 103359845691236916270005420886293061281368263471845448279855946394531250, -3993558675585831919973605001813276532326292885934464373884268722794718750, 83832389176247515253189196480455786065824463879054932596114704927571390625, -1101977288759423115916484463959767795703317649005495798822550381533967842875, 9865413224996821913093061266347613112205666995211786544733850490740903131000, -63509799534443193918054738326913581450607131662586232327271654588312820660616, 305080179205137176095342856930260393560550505786421683990077008259370104309410, -1122099959965657526344757894103766710826629020929459670948834078441128579791750, 3217185258543282976637373169047731813093578126603884759856059695249999306047500, -7276835259402589085458442196686990645669654847254510877738804991391058411412500, 13076651508858985557227319801010895818335803028865299751194038965212274849406250, -18719023753854332443081892087572754119280558462994056787647908433841920235468750, 21307327225910629020600045595173860611314592374884901403059587980149276271875000, -19156728115567236234371593788102013666866274144599851347429312225076676478125000, 13430107219321888006291381503763318985372222835071804983658005207838642173828125, -7186150593017474773099947110903785965879266917528290917207712073663895771484375, 2834031566467842832851805860262261718160136985649155528263587796394390332031250, -776308737033643856044315139790308583914612827783322139063211433790569824218750, 131897976398031751060811874321878385924211075364673046295410087596954345703125, -10468093364923154846096180501736379835254847251164527483762705364837646484375)) / 5456052820246562178600318839637653652351064473600000000
+    Poly = (u0, u1, u2, u3, u4, u5, u6, u7, u8, u9, u10, u11, u12, u13, u14, u15, u16,  u17, u18, u19, u20, u21)
     return split_evalpoly(-p/v, Poly)
 end
 

src/asymptotics.jl

@@ -19,11 +19,11 @@
 
 besseljy_large_argument_min(x::Float32) = 15.0f0
 besseljy_large_argument_min(x::Float64) = 20.0
-besseljy_large_argument_min(x) = 40.0
+#besseljy_large_argument_min(x) = 40.0
 
 besseljy_large_argument_cutoff(v, x::Float32) = (x > 1.2f0*v && x > besseljy_large_argument_min(x))
 besseljy_large_argument_cutoff(v, x::Float64) = (x > 1.65*v && x > besseljy_large_argument_min(x))
-besseljy_large_argument_cutoff(v, x) = (x > 4*v && x > besseljy_large_argument_min(x))
+#besseljy_large_argument_cutoff(v, x) = (x > 4*v && x > besseljy_large_argument_min(x))
 
 """
     besseljy_large_argument(nu, x::T)

src/besselj.jl

@@ -317,7 +317,7 @@ end
 besselj_series_cutoff(v, x::Float32) = (x < 20.0) || v > (14.4 + x*(-0.455 + 0.027*x))
 besselj_series_cutoff(v, x::Float64) = (x < 7.0) || v > (2 + x*(0.109 + 0.062*x))
 # cutoff for Float128 for ~1e-35 relative error 
-besselj_series_cutoff(v, x::AbstractFloat) = (x < 4.0) || v > (x*(0.08 + 0.12*x))
+#besselj_series_cutoff(v, x::AbstractFloat) = (x < 4.0) || v > (x*(0.08 + 0.12*x))
 
 #=
 # this needs a better way to sum these as it produces large errors

test/besselj_test.jl

@@ -13,6 +13,7 @@ j0_big = besselj0.(big.(x))
 @test j0_64[1] isa Float64
 @test j0_32[1] isa Float32
 @test j0_big[1] isa BigFloat
+@test besselj0(Float16(1.5)) isa Float16
 
 # test against SpecialFunctions.jl
 @test j0_32 ≈ j0_SpecialFunctions
@@ -48,6 +49,7 @@ j1_32 = besselj1.(Float32.(x))
 # make sure output types match input types
 @test j1_64[1] isa Float64
 @test j1_32[1] isa Float32
+@test besselj1(Float16(1.5)) isa Float16
 
 # test against SpecialFunctions.jl
 @test j1_64 ≈ j1_SpecialFunctions

test/bessely_test.jl

@@ -1,5 +1,5 @@
 # general array for testing input to SpecialFunctions.jl
-#=
+
 x = 0.01:0.01:150.0
 
 ### Tests for bessely0
@@ -14,6 +14,7 @@ y0_big = bessely0.(big.(x))
 @test y0_64[1] isa Float64
 @test y0_32[1] isa Float32
 @test y0_big[1] isa BigFloat
+@test bessely0(Float16(1.5)) isa Float16
 
 # test against SpecialFunctions.jl
 @test y0_SpecialFunctions ≈ y0_64
@@ -47,6 +48,7 @@ y1_32 = bessely1.(Float32.(x))
 # make sure output types match input types
 @test y1_64[1] isa Float64
 @test y1_32[1] isa Float32
+@test bessely1(Float16(1.5)) isa Float16
 
 # test against SpecialFunctions.jl
 @test y1_64 ≈ y1_SpecialFunctions
@@ -65,7 +67,6 @@ y1_32 = bessely1.(Float32.(x))
 # test that Inf inputs go to zero
 @test bessely1(Inf32) == zero(Float32)
 @test bessely1(Inf64) == zero(Float64)
-=#
 
 ## Tests for bessely