Thu Sep 13 19:29:24 2018 TRUNCATED_NORMAL_RULE_TEST: Python version: 3.6.5 Test the functions used by TRUNCATED_NORMAL_RULE. I4_UNIFORM_AB_TEST Python version: 3.6.5 I4_UNIFORM_AB computes pseudorandom values in an interval [A,B]. The lower endpoint A = -100 The upper endpoint B = 200 The initial seed is 123456789 1 -35 2 187 3 149 4 69 5 25 6 -81 7 -23 8 -67 9 -87 10 90 11 -82 12 35 13 20 14 127 15 139 16 -100 17 170 18 5 19 -72 20 -96 I4_UNIFORM_AB_TEST: Normal end of execution. R8_CHOOSE_TEST Python version: 3.6.5 R8_CHOOSE evaluates C(N,K). N K CNK 0 0 1 1 0 1 1 1 1 2 0 1 2 1 2 2 2 1 3 0 1 3 1 3 3 2 3 3 3 1 4 0 1 4 1 4 4 2 6 4 3 4 4 4 1 5 0 1 5 1 5 5 2 10 5 3 10 5 4 5 5 5 1 R8_CHOOSE_TEST Normal end of execution. R8_FACTORIAL_TEST Python version: 3.6.5 R8_FACTORIAL evaluates the factorial function. N Exact Computed 0 1 1 1 1 1 2 2 2 3 6 6 4 24 24 5 120 120 6 720 720 7 5040 5040 8 40320 40320 9 362880 362880 10 3628800 3628800 11 39916800 39916800 12 479001600 479001600 13 6227020800 6227020800 14 87178291200 87178291200 15 1307674368000 1307674368000 16 20922789888000 20922789888000 17 355687428096000 355687428096000 18 6402373705728000 6402373705728000 19 1.21645100408832e+17 1.21645100408832e+17 20 2.43290200817664e+18 2.43290200817664e+18 25 1.551121004333099e+25 1.551121004333099e+25 50 3.041409320171338e+64 3.041409320171338e+64 100 9.332621544394415e+157 9.33262154439441e+157 150 5.713383956445855e+262 5.71338395644585e+262 R8_FACTORIAL_TEST Normal end of execution. R8_FACTORIAL2_TEST Python version: 3.6.5 R8_FACTORIAL2 evaluates the double factorial function. N Exact Computed 0 1 1 1 1 1 2 2 2 3 3 3 4 8 8 5 15 15 6 48 48 7 105 105 8 384 384 9 945 945 10 3840 3840 11 10395 10395 12 46080 46080 13 135135 135135 14 645120 645120 15 2027025 2027025 R8_FACTORIAL2_TEST Normal end of execution. R8_HUGE_TEST Python version: 3.6.5 R8_HUGE returns a "huge" R8; R8_HUGE = 1.79769e+308 R8_HUGE_TEST Normal end of execution. R8_MOP_TEST Python version: 3.6.5 R8_MOP evaluates (-1.0)^I4 as an R8. I4 R8_MOP(I4) -57 -1.0 92 1.0 66 1.0 12 1.0 -17 -1.0 -87 -1.0 -49 -1.0 -78 1.0 -92 1.0 27 -1.0 R8_MOP_TEST Normal end of execution. R8MAT_PRINT_TEST Python version: 3.6.5 R8MAT_PRINT prints an R8MAT. Here is an R8MAT: Col: 0 1 2 3 4 Row 0 : 11 12 13 14 15 1 : 21 22 23 24 25 2 : 31 32 33 34 35 3 : 41 42 43 44 45 Col: 5 Row 0 : 16 1 : 26 2 : 36 3 : 46 R8MAT_PRINT_TEST: Normal end of execution. R8MAT_PRINT_SOME_TEST Python version: 3.6.5 R8MAT_PRINT_SOME prints some of an R8MAT. Here is an R8MAT: Col: 3 4 5 Row 0 : 14 15 16 1 : 24 25 26 2 : 34 35 36 R8MAT_PRINT_SOME_TEST: Normal end of execution. R8VEC_PRINT_TEST Python version: 3.6.5 R8VEC_PRINT prints an R8VEC. Here is an R8VEC: 0: 123.456 1: 5e-06 2: -1e+06 3: 3.14159 R8VEC_PRINT_TEST: Normal end of execution. R8VEC_WRITE_TEST: Python version: 3.6.5 Test R8VEC_WRITE, which writes an R8VEC to a file. Created file "r8vec_write_test.txt". R8VEC_WRITE_TEST: Normal end of execution. RULE_WRITE_TEST: Python version: 3.6.5 RULE_WRITE writes a quadrature rule to three files. Creating quadrature files. Common header is "rule_write_test". Weight file will be "rule_write_test_w.txt". Abscissa file will be "rule_write_test_x.txt". Region file will be "rule_write_test_r.txt". The quadrature rule has been written to files. RULE_WRITE_TEST: Normal end of execution. TIMESTAMP_TEST: Python version: 3.6.5 TIMESTAMP prints a timestamp of the current date and time. Thu Sep 13 19:29:24 2018 TIMESTAMP_TEST: Normal end of execution. NORMAL_01_CDF_TEST Python version: 3.6.5 NORMAL_01_CDF evaluates the CDF; X CDF CDF (exact) (computed) 0 0.5 0.5 0.1 0.539827837277029 0.5398278372805048 0.2 0.579259709439103 0.5792597094424672 0.3 0.6179114221889526 0.6179114221891665 0.4 0.6554217416103242 0.6554217416083834 0.5 0.6914624612740131 0.6914624612735877 0.6 0.725746882249927 0.7257468822526401 0.7 0.758036347776927 0.7580363477802913 0.8 0.7881446014166033 0.7881446014178579 0.9 0.8159398746532405 0.8159398746539517 1 0.8413447460685429 0.8413447460717163 1.5 0.9331927987311419 0.9331927987330156 2 0.9772498680518208 0.9772498680509744 2.5 0.993790334674224 0.9937903346744605 3 0.9986501019683699 0.9986501019683744 3.5 0.9997673709209645 0.9997673709209559 4 0.9999683287581669 0.9999683287581664 NORMAL_01_CDF_TEST: Normal end of execution. NORMAL_01_MOMENT_TEST Python version: 3.6.5 NORMAL_01_MOMENT evaluates moments of the Normal 01 PDF; Order Moment 0 1 1 0 2 1 3 0 4 3 5 0 6 15 7 0 8 105 9 0 10 945 NORMAL_01_MOMENT_TEST: Normal end of execution. NORMAL_01_PDF_TEST Python version: 3.6.5 NORMAL_01_PDF evaluates the PDF; X PDF -2 0.05399096651318806 -1.9 0.0656158147746766 -1.8 0.07895015830089415 -1.7 0.09404907737688695 -1.6 0.1109208346794555 -1.5 0.1295175956658917 -1.4 0.1497274656357449 -1.3 0.1713685920478074 -1.2 0.194186054983213 -1.1 0.2178521770325506 -1 0.2419707245191434 -0.9 0.2660852498987548 -0.8 0.2896915527614827 -0.7 0.3122539333667613 -0.6 0.3332246028917997 -0.5 0.3520653267642995 -0.4 0.3682701403033233 -0.3 0.3813878154605241 -0.2 0.3910426939754559 -0.1 0.3969525474770118 0 0.3989422804014327 0.1 0.3969525474770118 0.2 0.3910426939754559 0.3 0.3813878154605241 0.4 0.3682701403033233 0.5 0.3520653267642995 0.6 0.3332246028917997 0.7 0.3122539333667613 0.8 0.2896915527614827 0.9 0.2660852498987548 1 0.2419707245191434 1.1 0.2178521770325506 1.2 0.194186054983213 1.3 0.1713685920478074 1.4 0.1497274656357449 1.5 0.1295175956658917 1.6 0.1109208346794555 1.7 0.09404907737688695 1.8 0.07895015830089415 1.9 0.0656158147746766 2 0.05399096651318806 NORMAL_01_PDF_TEST: Normal end of execution. NORMAL_MS_MOMENT_TEST Python version: 3.6.5 NORMAL_MS_MOMENT evaluates moments of the Normal MS distribution. Mu = 0, Sigma = 1 Order Moment 0 1 1 1 0 0 2 1 1 3 0 0 4 3 3 5 0 0 6 15 15 7 0 0 8 105 105 Mu = 2, Sigma = 1 Order Moment 0 1 1 1 2 2 2 5 5 3 14 14 4 43 43 5 142 142 6 499 499 7 1850 1850 8 7193 7193 Mu = 10, Sigma = 2 Order Moment 0 1 1 1 10 10 2 104 104 3 1120 1120 4 12448 12448 5 142400 142400 6 1.67296e+06 1.67296e+06 7 2.01472e+07 2.01472e+07 8 2.48315e+08 2.48315e+08 Mu = 0, Sigma = 2 Order Moment 0 1 1 1 0 0 2 4 4 3 0 0 4 48 48 5 0 0 6 960 960 7 0 0 8 26880 26880 NORMAL_MS_MOMENT_TEST: Normal end of execution. TRUNCATED_NORMAL_A_MOMENT_TEST Python version: 3.6.5 TRUNCATED_NORMAL_A_MOMENT evaluates moments of the lower Truncated Normal distribution. Test = 0, Mu = 0, Sigma = 1, A = 0 Order Moment 0 1 1 0.797885 2 1 3 1.59577 4 3 5 6.38308 6 15 7 38.2985 8 105 Test = 1, Mu = 0, Sigma = 1, A = -10 Order Moment 0 1 1 7.6946e-23 2 1 3 7.84849e-21 4 3 5 8.00854e-19 6 15 7 8.17511e-17 8 105 Test = 2, Mu = 0, Sigma = 1, A = 10 Order Moment 0 1 1 10.0981 2 101.981 3 1030.01 4 10404 5 105101 6 1.06183e+06 7 1.07287e+07 8 1.08414e+08 Test = 3, Mu = 0, Sigma = 2, A = -10 Order Moment 0 1 1 2.97344e-06 2 3.99997 3 0.000321132 4 47.9967 5 0.0348725 6 959.636 7 3.81038 8 26840.1 Test = 4, Mu = 0, Sigma = 2, A = 10 Order Moment 0 1 1 10.373 2 107.73 3 1120.28 4 11665.8 5 121655 6 1.27062e+06 7 1.32927e+07 8 1.39307e+08 Test = 5, Mu = -5, Sigma = 1, A = -10 Order Moment 0 1 1 -5 2 26 3 -140 4 777.997 5 -4449.97 6 26139.7 7 -157397 8 969947 TRUNCATED_NORMAL_A_MOMENT_TEST: Normal end of execution. TRUNCATED_NORMAL_AB_MOMENT_TEST Python version: 3.6.5 TRUNCATED_NORMAL_AB_MOMENT evaluates moments of the Truncated Normal distribution. Test = 0, Mu = 0, Sigma = 1, A = -1, B = 1 Order Moment 0 1 1 0 2 0.291125 3 0 4 0.1645 5 0 6 0.113627 7 0 8 0.086514 Test = 1, Mu = 0, Sigma = 1, A = 0, B = 1 Order Moment 0 1 1 0.459862 2 0.291125 3 0.21085 4 0.1645 5 0.134523 6 0.113627 7 0.0982649 8 0.086514 Test = 2, Mu = 0, Sigma = 1, A = -1, B = 0 Order Moment 0 1 1 -0.459862 2 0.291125 3 -0.21085 4 0.1645 5 -0.134523 6 0.113627 7 -0.0982649 8 0.086514 Test = 3, Mu = 0, Sigma = 2, A = -1, B = 1 Order Moment 0 1 1 0 2 0.322357 3 0 4 0.190636 5 0 6 0.135077 7 0 8 0.104524 Test = 4, Mu = 1, Sigma = 1, A = 0, B = 2 Order Moment 0 1 1 1 2 1.29113 3 1.87338 4 2.91125 5 4.73375 6 7.94801 7 13.6665 8 23.9346 Test = 5, Mu = 0, Sigma = 1, A = 0.5, B = 2 Order Moment 0 1 1 1.04299 2 1.23812 3 1.63828 4 2.35698 5 3.60741 6 5.77795 7 9.57285 8 16.2735 Test = 6, Mu = 0, Sigma = 1, A = -2, B = 2 Order Moment 0 1 1 0 2 0.773741 3 0 4 1.41619 5 0 6 3.46081 7 0 8 9.74509 Test = 7, Mu = 0, Sigma = 1, A = -4, B = 4 Order Moment 0 1 1 0 2 0.998929 3 0 4 2.97966 5 0 6 14.6242 7 0 8 97.9836 Test = 8, Mu = 5, Sigma = 0.5, A = 4, B = 7 Order Moment 0 1 1 5.02756 2 25.4978 3 130.441 4 673.075 5 3502.72 6 18382.1 7 97269.7 8 518913 TRUNCATED_NORMAL_AB_MOMENT_TEST: Normal end of execution. TRUNCATED_NORMAL_B_MOMENT_TEST Python version: 3.6.5 TRUNCATED_NORMAL_B_MOMENT evaluates moments of the upper Truncated Normal distribution. Test = 0, Mu = 0, Sigma = 1, B = 0 Order Moment 0 1 1 -0.797885 2 1 3 -1.59577 4 3 5 -6.38308 6 15 7 -38.2985 8 105 Test = 1, Mu = 0, Sigma = 1, B = 10 Order Moment 0 1 1 -7.6946e-23 2 1 3 -7.84849e-21 4 3 5 -8.00854e-19 6 15 7 -8.17511e-17 8 105 Test = 2, Mu = 0, Sigma = 1, B = -10 Order Moment 0 1 1 -10.0981 2 101.981 3 -1030.01 4 10404 5 -105101 6 1.06183e+06 7 -1.07287e+07 8 1.08414e+08 Test = 3, Mu = 0, Sigma = 2, B = 10 Order Moment 0 1 1 -2.97344e-06 2 3.99997 3 -0.000321132 4 47.9967 5 -0.0348725 6 959.636 7 -3.81038 8 26840.1 Test = 4, Mu = 0, Sigma = 2, B = -10 Order Moment 0 1 1 -10.373 2 107.73 3 -1120.28 4 11665.8 5 -121655 6 1.27062e+06 7 -1.32927e+07 8 1.39307e+08 Test = 5, Mu = 5, Sigma = 1, B = 10 Order Moment 0 1 1 5 2 26 3 140 4 777.997 5 4449.97 6 26139.7 7 157397 8 969947 TRUNCATED_NORMAL_B_MOMENT_TEST: Normal end of execution. MOMENT_METHOD_TEST Python version: 3.6.5 MOMENT_METHOD uses the method of moments for a quadrature rule. Computed Correct I X X 0 -2.85697 -2.85697 1 -1.35563 -1.35563 2 -4.25837e-16 0 3 1.35563 1.35563 4 2.85697 2.85697 Computed Correct I W W 0 0.0112574 0.0112574 1 0.222076 0.222076 2 0.533333 0.533333 3 0.222076 0.222076 4 0.0112574 0.0112574 MOMENT_METHOD_TEST: Normal end of execution. OPTION0_TEST: Python version: 3.6.5 Get a quadrature rule for the untruncated normal distribution. TRUNCATED_NORMAL_RULE Python version: 3.6.5 For the (truncated) Gaussian probability density function pdf(x) = exp(-0.5*((x-MU)/SIGMA)^2) / SIGMA / sqrt ( 2 * pi ) compute an N-point quadrature rule for approximating Integral ( A <= x <= B ) f(x) pdf(x) dx The value of OPTION determines the truncation interval [A,B]: 0: (-oo,+oo) 1: [A,+oo) 2: (-oo,B] 3: [A,B] The user specifies OPTION, N, MU, SIGMA, A, B and FILENAME. HEADER is used to generate 3 files: header_w.txt - the weight file header_x.txt - the abscissa file. header_r.txt - the region file, listing A and B. OPTION = 0 N = 5 MU = 1 SIGMA = 2 A = -oo B = +oo HEADER = "option0" Creating quadrature files. Common header is "option0". Weight file will be "option0_w.txt". Abscissa file will be "option0_x.txt". Region file will be "option0_r.txt". TRUNCATED_NORMAL_RULE: Normal end of execution. OPTION0_TEST: Normal end of execution. OPTION1_TEST: Python version: 3.6.5 Get a quadrature rule for the lower truncated normal distribution. TRUNCATED_NORMAL_RULE Python version: 3.6.5 For the (truncated) Gaussian probability density function pdf(x) = exp(-0.5*((x-MU)/SIGMA)^2) / SIGMA / sqrt ( 2 * pi ) compute an N-point quadrature rule for approximating Integral ( A <= x <= B ) f(x) pdf(x) dx The value of OPTION determines the truncation interval [A,B]: 0: (-oo,+oo) 1: [A,+oo) 2: (-oo,B] 3: [A,B] The user specifies OPTION, N, MU, SIGMA, A, B and FILENAME. HEADER is used to generate 3 files: header_w.txt - the weight file header_x.txt - the abscissa file. header_r.txt - the region file, listing A and B. OPTION = 1 N = 9 MU = 2 SIGMA = 0.5 A = 0 B = +oo HEADER = "option1" Creating quadrature files. Common header is "option1". Weight file will be "option1_w.txt". Abscissa file will be "option1_x.txt". Region file will be "option1_r.txt". TRUNCATED_NORMAL_RULE: Normal end of execution. OPTION1_TEST: Normal end of execution. OPTION2_TEST: Python version: 3.6.5 Get a quadrature rule for the upper truncated normal distribution. TRUNCATED_NORMAL_RULE Python version: 3.6.5 For the (truncated) Gaussian probability density function pdf(x) = exp(-0.5*((x-MU)/SIGMA)^2) / SIGMA / sqrt ( 2 * pi ) compute an N-point quadrature rule for approximating Integral ( A <= x <= B ) f(x) pdf(x) dx The value of OPTION determines the truncation interval [A,B]: 0: (-oo,+oo) 1: [A,+oo) 2: (-oo,B] 3: [A,B] The user specifies OPTION, N, MU, SIGMA, A, B and FILENAME. HEADER is used to generate 3 files: header_w.txt - the weight file header_x.txt - the abscissa file. header_r.txt - the region file, listing A and B. OPTION = 2 N = 9 MU = 2 SIGMA = 0.5 A = -oo B = 3 HEADER = "option2" Creating quadrature files. Common header is "option2". Weight file will be "option2_w.txt". Abscissa file will be "option2_x.txt". Region file will be "option2_r.txt". TRUNCATED_NORMAL_RULE: Normal end of execution. OPTION2_TEST: Normal end of execution. OPTION3_TEST: Python version: 3.6.5 Get a quadrature rule for the truncated normal distribution. TRUNCATED_NORMAL_RULE Python version: 3.6.5 For the (truncated) Gaussian probability density function pdf(x) = exp(-0.5*((x-MU)/SIGMA)^2) / SIGMA / sqrt ( 2 * pi ) compute an N-point quadrature rule for approximating Integral ( A <= x <= B ) f(x) pdf(x) dx The value of OPTION determines the truncation interval [A,B]: 0: (-oo,+oo) 1: [A,+oo) 2: (-oo,B] 3: [A,B] The user specifies OPTION, N, MU, SIGMA, A, B and FILENAME. HEADER is used to generate 3 files: header_w.txt - the weight file header_x.txt - the abscissa file. header_r.txt - the region file, listing A and B. OPTION = 3 N = 5 MU = 100 SIGMA = 25 A = 50 B = 150 HEADER = "option3" Creating quadrature files. Common header is "option3". Weight file will be "option3_w.txt". Abscissa file will be "option3_x.txt". Region file will be "option3_r.txt". TRUNCATED_NORMAL_RULE: Normal end of execution. OPTION3_TEST: Normal end of execution. TRUNCATED_NORMAL_RULE_TEST: Normal end of execution. Thu Sep 13 19:29:24 2018