import drjit as dr import mitsuba as mi import numpy as np from math import nan,inf from mitsuba import Float use_special_form = True #C = 5 A_i0 = Float([ -4.41534164647933937950E-18, 3.33079451882223809783E-17, -2.43127984654795469359E-16, 1.71539128555513303061E-15, -1.16853328779934516808E-14, 7.67618549860493561688E-14, -4.85644678311192946090E-13, 2.95505266312963983461E-12, -1.72682629144155570723E-11, 9.67580903537323691224E-11, -5.18979560163526290666E-10, 2.65982372468238665035E-9, -1.30002500998624804212E-8, 6.04699502254191894932E-8, -2.67079385394061173391E-7, 1.11738753912010371815E-6, -4.41673835845875056359E-6, 1.64484480707288970893E-5, -5.75419501008210370398E-5, 1.88502885095841655729E-4, -5.76375574538582365885E-4, 1.63947561694133579842E-3, -4.32430999505057594430E-3, 1.05464603945949983183E-2, -2.37374148058994688156E-2, 4.93052842396707084878E-2, -9.49010970480476444210E-2, 1.71620901522208775349E-1, -3.04682672343198398683E-1, 6.76795274409476084995E-1]) B_i0 = Float([ -7.23318048787475395456E-18, -4.83050448594418207126E-18, 4.46562142029675999901E-17, 3.46122286769746109310E-17, -2.82762398051658348494E-16, -3.42548561967721913462E-16, 1.77256013305652638360E-15, 3.81168066935262242075E-15, -9.55484669882830764870E-15, -4.15056934728722208663E-14, 1.54008621752140982691E-14, 3.85277838274214270114E-13, 7.18012445138366623367E-13, -1.79417853150680611778E-12, -1.32158118404477131188E-11, -3.14991652796324136454E-11, 1.18891471078464383424E-11, 4.94060238822496958910E-10, 3.39623202570838634515E-9, 2.26666899049817806459E-8, 2.04891858946906374183E-7, 2.89137052083475648297E-6, 6.88975834691682398426E-5, 3.36911647825569408990E-3, 8.04490411014108831608E-1]) A_k0 = Float([ 1.37446543561352307156E-16, 4.25981614279661018399E-14, 1.03496952576338420167E-11, 1.90451637722020886025E-9, 2.53479107902614945675E-7, 2.28621210311945178607E-5, 1.26461541144692592338E-3, 3.59799365153615016266E-2, 3.44289899924628486886E-1, -5.35327393233902768720E-1]) B_k0 = Float([ 5.30043377268626276149E-18, -1.64758043015242134646E-17, 5.21039150503902756861E-17, -1.67823109680541210385E-16, 5.51205597852431940784E-16, -1.84859337734377901440E-15, 6.34007647740507060557E-15, -2.22751332699166985548E-14, 8.03289077536357521100E-14, -2.98009692317273043925E-13, 1.14034058820847496303E-12, -4.51459788337394416547E-12, 1.85594911495471785253E-11, -7.95748924447710747776E-11, 3.57739728140030116597E-10, -1.69753450938905987466E-9, 8.57403401741422608519E-9, -4.66048989768794782956E-8, 2.76681363944501510342E-7, -1.83175552271911948767E-6, 1.39498137188764993662E-5, -1.28495495816278026384E-4, 1.56988388573005337491E-3, -3.14481013119645005427E-2, 2.44030308206595545468E0]) A_i1 = Float([ 2.77791411276104639959E-18, -2.11142121435816608115E-17, 1.55363195773620046921E-16, -1.10559694773538630805E-15, 7.60068429473540693410E-15, -5.04218550472791168711E-14, 3.22379336594557470981E-13, -1.98397439776494371520E-12, 1.17361862988909016308E-11, -6.66348972350202774223E-11, 3.62559028155211703701E-10, -1.88724975172282928790E-9, 9.38153738649577178388E-9, -4.44505912879632808065E-8, 2.00329475355213526229E-7, -8.56872026469545474066E-7, 3.47025130813767847674E-6, -1.32731636560394358279E-5, 4.78156510755005422638E-5, -1.61760815825896745588E-4, 5.12285956168575772895E-4, -1.51357245063125314899E-3, 4.15642294431288815669E-3, -1.05640848946261981558E-2, 2.47264490306265168283E-2, -5.29459812080949914269E-2, 1.02643658689847095384E-1, -1.76416518357834055153E-1, 2.52587186443633654823E-1]) B_i1 = Float([ 7.51729631084210481353E-18, 4.41434832307170791151E-18, -4.65030536848935832153E-17, -3.20952592199342395980E-17, 2.96262899764595013876E-16, 3.30820231092092828324E-16 -1.88035477551078244854E-15, -3.81440307243700780478E-15, 1.04202769841288027642E-14, 4.27244001671195135429E-14, -2.10154184277266431302E-14,-4.08355111109219731823E-13, -7.19855177624590851209E-13,2.03562854414708950722E-12, 1.41258074366137813316E-11, 3.25260358301548823856E-11, -1.89749581235054123450E-11, -5.58974346219658380687E-10, -3.83538038596423702205E-9, -2.63146884688951950684E-8, -2.51223623787020892529E-7,-3.88256480887769039346E-6, -1.10588938762623716291E-4, -9.76109749136146840777E-3, 7.78576235018280120474E-1]) A_k1 = Float([ -7.02386347938628759343E-18, -2.42744985051936593393E-15, -6.66690169419932900609E-13,-1.41148839263352776110E-10, -2.21338763073472585583E-8,-2.43340614156596823496E-6, -1.73028895751305206302E-4,-6.97572385963986435018E-3, -1.22611180822657148235E-1,-3.53155960776544875667E-1, 1.52530022733894777053E0]) B_k1 = Float([ -5.75674448366501715755E-18,1.79405087314755922667E-17, -5.68946255844285935196E-17,1.83809354436663880070E-16, -6.05704724837331885336E-16,2.03870316562433424052E-15, -7.01983709041831346144E-15,2.47715442448130437068E-14, -8.97670518232499435011E-14,3.34841966607842919884E-13, -1.28917396095102890680E-12,5.13963967348173025100E-12, -2.12996783842756842877E-11,9.21831518760500529508E-11, -4.19035475934189648750E-10,2.01504975519703286596E-9, -1.03457624656780970260E-8,5.74108412545004946722E-8, -3.50196060308781257119E-7,2.40648494783721712015E-6, -1.93619797416608296024E-5,1.95215518471351631108E-4, -2.85781685962277938680E-3,1.03923736576817238437E-1, 2.72062619048444266945E0]) """ Implementation of some special functions.""" """ Evaluate Chebyshev polynomial at x/2 argument.""" @dr.syntax def chebyshev(x , coeffs): b0 = Float(coeffs[0]) b1 = Float(0) b2 = Float(0) i = 0 while dr.hint(i < dr.width(coeffs), mode = 'scalar'): b2 = b1 b1 = b0 b0 = x * b1 - (b2 - coeffs[i]) i += 1 return (b0 - b2) * 0.5; @dr.syntax def Gσ(y : Float, z : Float) -> Float: # Multiplied with 2*pi version result = Float(0) #if dr.hint(use_special_form, mode = 'scalar'): c_i0_yz_below8 = chebyshev(y * z/2.0 - 2.0, A_i0) c_i0_z_above8 = chebyshev(32.0/z - 2.0, B_i0) i0_div = Float(0) c_i0_yz_above8 = Float(0) c_i0_z_below8 = Float(0) if (y * z > 8): c_i0_yz_above8 = chebyshev(32.0/(y * z) - 2.0, B_i0) i0_div = c_i0_yz_above8/c_i0_z_above8 / dr.sqrt(y) elif (z <= 8): c_i0_z_below8 = chebyshev(z/2.0 - 2.0, A_i0) i0_div = c_i0_yz_below8/c_i0_z_below8 else: i0_div = c_i0_yz_below8/c_i0_z_above8 / dr.sqrt(z) result = (k0(y * z) - k0(z) * i0_div * dr.exp((y-1) * z)) #else: # result = k0(y * z) - k0(z) * i0(y * z) / i0(z) valid_region = (y<1) & (y>0) result = dr.select(valid_region, result, 0) return result @dr.syntax def dGσ(y: Float, z: Float) -> Float: # Multiplied with 2*pi version result = Float(0) #if use_special_form: c_i1_yz_below8 = chebyshev(y * z/2.0 - 2.0, A_i1) c_i1_z_above8 = chebyshev(32.0/z - 2.0, B_i1) i1_div = Float(0) c_i1_yz_above8 = Float(0) c_i1_z_below8 = Float(0) if (y * z > 8): c_i1_yz_above8 = chebyshev(32.0/(y * z) - 2.0, B_i1) i1_div = c_i1_yz_above8/c_i1_z_above8 / dr.sqrt(y) elif z <= 8: c_i1_z_below8 = chebyshev(z/2.0 - 2.0, A_i1) i1_div = c_i1_yz_below8/c_i1_z_below8 * y else: i1_div = c_i1_yz_below8/c_i1_z_above8 * y * z * dr.sqrt(z) result = k1(y * z) - k1(z) * i1_div * dr.exp((y-1) * z) #else: # result = k1(y * z) - k1(z) * i1(y * z) / i1(z) valid_region = (y<1) & (y>=0) result = dr.select(valid_region, result, 0) return result @dr.syntax def Gσr_int(y : Float, z : Float) -> Float: # This returns sigma |G|. #if use_special_form: c_i1_yz_below8 = chebyshev(y * z/2.0 - 2.0, A_i1) c_i0_z_above8 = chebyshev(32.0/z - 2.0, B_i0) c_i1_yz_above8 = Float(0) c_i0_z_below8 = Float(0) i_div = Float(0) if y * z > 8: c_i1_yz_above8 = chebyshev(32.0/(y * z) - 2.0, B_i1) i_div = c_i1_yz_above8/c_i0_z_above8 / dr.sqrt(y) elif z <= 8: c_i0_z_below8 = chebyshev(z/2.0 - 2.0, A_i0) i_div = c_i1_yz_below8/c_i0_z_below8 * z * y else: i_div = c_i1_yz_below8/c_i0_z_above8 * z * y * dr.sqrt(z) result = 1 - y * z * (i_div * k0(z) * dr.exp((y-1) * z) + k1(y * z)) #else: # result = 1 - y * z * (k0(z) * i1(y * z) / i0(z) + k1(y * z)) return result def Qσ(y,z): return 1 - Gσr_int(y,z) * dr.square(z) @dr.syntax def i0(x_ : Float) -> Float: x = dr.abs(x_) x = dr.select(x == 0, dr.epsilon(Float), x) if x<=8: result = chebyshev(x/2.0 - 2.0, A_i0) else: result = chebyshev(32.0/x - 2.0, B_i0) / dr.sqrt(x) if x_==0: result = Float(1) else: result *= dr.exp(x) return result @dr.syntax def k0(x_): x = dr.abs(x_) if x == Float(0): x = Float(dr.epsilon(Float)) if x <= 2: result = chebyshev((dr.sqr(x) - 2), A_k0) - dr.log(0.5 * x) * i0(x) else: result = dr.exp(-x) * chebyshev(8.0 / x - 2, B_k0) / dr.sqrt(x) if x_ == 0: result = Float(dr.inf) if x_ < 0: result = Float(dr.nan) return result @dr.syntax def i1(x_): x = dr.abs(x_) if x == 0: x += dr.epsilon(Float) if x<=8: result = chebyshev(x/2.0 - 2.0, A_i1) * x else: result = chebyshev(32.0 / x - 2.0, B_i1) / dr.sqrt(x) if x_ == 0: result = Float(0) if x_ < 0: result = -result return result * dr.exp(x) @dr.syntax def k1(x_): x = dr.abs(x_) if x == 0: x += dr.epsilon(Float) if x <= 2: result = dr.log(x * 0.5) * i1(x) + chebyshev(dr.sqr(x) - 2.0, A_k1) / x else: result = dr.exp(-x) * chebyshev(8.0 / x -2, B_k1) / dr.sqrt(x) if x_ == 0: result = Float(dr.inf) if x_ < 0: result = Float(dr.nan) return result ## Remove the exponential terms # divided by exp(x) @dr.syntax def i0_(x_): x = dr.abs(x_) x = dr.select(x == 0, dr.epsilon(Float), x) if x<=8: result = chebyshev(x/2.0 - 2.0, A_i0) else: result = chebyshev(32.0/x - 2.0, B_i0) / dr.sqrt(x) if x_==0: result = Float(1) return result # multiplied by exp(x) @dr.syntax def k0_(x_): x = dr.abs(x_) if x == 0: x += dr.epsilon(Float) if x <= 2: result = dr.exp(x) (chebyshev((dr.sqr(x) - 2), A_k0) - dr.log(0.5 * x) * i0(x)) else: result = chebyshev(8.0 / x - 2, B_k0) / dr.sqrt(x), if x_ == 0: result = Float(dr.inf) if x_ < 0: result = Float(dr.nan) return result # divided by exp(x) @dr.syntax def i1_(x_): x = dr.abs(x_) if x == 0: x += dr.epsilon(Float) if x<=8: result = chebyshev(x/2.0 - 2.0, A_i1) * x else: result = chebyshev(32.0 / x - 2.0, B_i1) / dr.sqrt(x) if x_ == 0: result = Float(0) if x_ < 0: result = -result return result # multiplied by exp(x) @dr.syntax def k1_(x_): x = dr.abs(x_) if x == 0: x += dr.epsilon(Float) if x <= 2: result = dr.exp(x) * (dr.log(x * 0.5) * i1(x) + chebyshev(dr.sqr(x) - 2.0, A_k1) / x) else: result = chebyshev(8.0 / x -2, B_k1) / dr.sqrt(x) if x_ == 0: result = Float(inf) if x_ < 0: result = Float(nan) return result