Thu Dec 26 10:23:58 2024 scipy_test(): python version: 3.10.12 numpy version: 1.26.4 scipy version: 1.8.0 scipy() is an package of advanced mathematical functions. bessel_j_test(): Plot some Bessel Jn(x) functions for n = 0, 1, ... Graphics saved as "bessel_j_test.png" brentq_test(): brentq() seeks a root of a given function. Graphics saved as "brentq_test.png" Root found at -0.5933306271014237 f(x) = -3.3029134982598407e-15 ConvexHull_test(): python version: 3.10.12 numpy version: 1.26.4 scipy version: 1.8.0 ConvexHull() is a scipy.spatial function which computes the convex hull of a set of points. Graphics saved as "ConvexHull_eddy.png" Graphics saved as "ConvexHull_graham.png" Graphics saved as "ConvexHull_kn57.png" Graphics saved as "ConvexHull_random.png" contour_himmelblau_test(): Draw a contour plot of the Himmelblau function. Graphics saved as "contour_himmelblau_test.png" drum_normal_modes_test(): Display normal modes of a vibrating drumhead. Graphics saved as "drum_contour.png" Graphics saved as "drum_contourf.png" Graphics saved as "drum_surface.png" fft_co2_test(): Use fft() and ifft() to analyze variation in CO2 levels. Graphics saved as "fft_co2_test.png" Graphics saved as "fft_co2_detrended.png" Graphics saved as "fft_co2_periodogram.png" fft_el_nino_test(): Use fft() to interpolate el nino pressure variation data. Graphics saved as "fft_el_nino_test.png" [0. 0.00297619 0.00396825 0.00446429 0.0047619 0.00496032 0.00510204 0.00520833 0.00529101 0.00535714 0.00541126 0.00545635 0.00549451 0.00552721 0.00555556 0.00558036 0.00560224 0.00562169 0.0056391 0.00565476 0.00566893 0.00568182 0.00569358 0.00570437 0.00571429 0.00572344 0.00573192 0.0057398 0.00574713 0.00575397 0.00576037 0.00576637 0.00577201 0.00577731 0.00578231 0.00578704 0.00579151 0.00579574 0.00579976 0.00580357 0.0058072 0.00581066 0.00581395 0.0058171 0.00582011 0.00582298 0.00582573 0.00582837 0.0058309 0.00583333 0.00583567 0.00583791 0.00584007 0.00584215 0.00584416 0.00584609 0.00584795 0.00584975 0.00585149 0.00585317 0.0058548 0.00585637 0.0058579 0.00585938 0.00586081 0.00586219 0.00586354 0.00586485 0.00586611 0.00586735 0.00586854 0.00586971 0.00587084 0.00587194 0.00587302 0.00587406 0.00587508 0.00587607 0.00587703 0.00587798 0.00587889 0.00587979 0.00588067 0.00588152] [ 2.12833333e+01 3.27523416e-01 6.08965906e-01 6.44235200e-01 -1.32885103e+00 2.96165616e-01 1.14461178e+00 4.74161225e-02 -6.92865989e-01 -2.41689826e-01 -1.45647039e-01 1.60682814e-01 -1.48442263e-02 4.99498773e-02 2.88396944e+00 -3.03351227e-01 -2.02207951e-01 -5.64855975e-01 2.41912477e-01 -4.26292763e-01 -2.35015264e-01 1.34541848e-02 2.34550179e-01 1.41258568e-01 -9.19459830e-02 3.36999342e-02 -6.23301596e-02 1.94532525e-02 7.44047619e-02 -4.48538256e-01 -2.59546801e-02 -2.25771474e-01 -3.74524935e-01 8.25818209e-02 -9.88520186e-02 3.87306173e-02 2.95394423e-01 5.81745998e-01 -1.70865873e-02 3.77916866e-03 -3.57657547e-01 1.34440340e-01 -2.09523810e-01 1.09933513e-01 1.65272034e-01 3.20194292e-01 -2.46591113e-02 2.67839955e-01 2.98471597e-01 -2.92443952e-01 -3.28442384e-01 -1.91276158e-01 2.97961151e-01 -1.20309757e-01 2.54031170e-01 2.79468260e-01 -4.47023810e-01 3.09818009e-01 -1.81517186e-01 -2.11586023e-01 3.43307558e-02 1.64869288e-01 -3.09753490e-01 8.41648629e-02 -6.90052401e-02 -6.14225025e-02 -5.90572111e-02 -9.83921461e-02 2.06444999e-01 -1.45989459e-01 4.21982938e-01 -5.61955705e-02 4.76410529e-02 1.97793951e-01 -2.98608803e-01 1.65795991e-01 -8.85499811e-02 -4.13218358e-02 -3.62686394e-01 1.27141484e-01 -2.40047863e-01 -1.11170111e-01 1.09094980e-01 2.88470501e-01 -2.88095238e-01 2.88470501e-01 1.09094980e-01 -1.11170111e-01 -2.40047863e-01 1.27141484e-01 -3.62686394e-01 -4.13218358e-02 -8.85499811e-02 1.65795991e-01 -2.98608803e-01 1.97793951e-01 4.76410529e-02 -5.61955705e-02 4.21982938e-01 -1.45989459e-01 2.06444999e-01 -9.83921461e-02 -5.90572111e-02 -6.14225025e-02 -6.90052401e-02 8.41648629e-02 -3.09753490e-01 1.64869288e-01 3.43307558e-02 -2.11586023e-01 -1.81517186e-01 3.09818009e-01 -4.47023810e-01 2.79468260e-01 2.54031170e-01 -1.20309757e-01 2.97961151e-01 -1.91276158e-01 -3.28442384e-01 -2.92443952e-01 2.98471597e-01 2.67839955e-01 -2.46591113e-02 3.20194292e-01 1.65272034e-01 1.09933513e-01 -2.09523810e-01 1.34440340e-01 -3.57657547e-01 3.77916866e-03 -1.70865873e-02 5.81745998e-01 2.95394423e-01 3.87306173e-02 -9.88520186e-02 8.25818209e-02 -3.74524935e-01 -2.25771474e-01 -2.59546801e-02 -4.48538256e-01 7.44047619e-02 1.94532525e-02 -6.23301596e-02 3.36999342e-02 -9.19459830e-02 1.41258568e-01 2.34550179e-01 1.34541848e-02 -2.35015264e-01 -4.26292763e-01 2.41912477e-01 -5.64855975e-01 -2.02207951e-01 -3.03351227e-01 2.88396944e+00 4.99498773e-02 -1.48442263e-02 1.60682814e-01 -1.45647039e-01 -2.41689826e-01 -6.92865989e-01 4.74161225e-02 1.14461178e+00 2.96165616e-01 -1.32885103e+00 6.44235200e-01 6.08965906e-01 3.27523416e-01] Graphics saved as "fft_el_nino_recovered.png" fft_test(): x: [ 1. 2. 1. -1. 1.5] y = fft(x): [ 4.5 -0.j 2.08155948-1.65109876j -1.83155948+1.60822041j -1.83155948-1.60822041j 2.08155948+1.65109876j] z = ifft(y): [ 1. +0.j 2. +0.j 1. +0.j -1. +0.j 1.5+0.j] interp1d_test(): interp1d() performas several types of 1D interpolation. Graphics saved as "interp1d_test.png" linalg_test(): Set up and solve a linear system A*x=b. Matrix A: [[6 7 8 2 2] [1 2 2 5 6] [5 5 5 5 7] [3 7 6 6 3] [8 8 8 8 4]] Right hand side b: [ 62 61 85 74 100] Computed solution x: [1. 2. 3. 4. 5.] ||error|| = 0.0 minimize_himmelblau_test(): Use minimize() to find a minimizer of the Himmelblau function. Starting at x = [0. 0.] f(x) = 170.0 Minimizer found at [2.99999994 1.99999999] f(x) = 1.3782267979368085e-13 minimize_scalar_polynomial(): minimize_scalar() seeks a minimizer of a scalar function. Graphics saved as "minimize_scalar_polynomial.png" Minimizer found at -2.8410443265926113 p(x) = -91.32163915433344 Specifying search interval of [0,6]: Minimizer found at -2.8410443265926113 p(x) = -91.32163915433344 ndimage_test(): Apply ndimage() functions to manipulate an image. Reading original image from "face.png" Graphics saved as "ndimage_rotated.png" Graphics saved as "ndimage_blurred.png" quad_test(): Use quad() to integrate a Bessel function jv(2.5,x). Integral estimate is 1.1178179380783253 Error estimate is 7.866317216380692e-09 solve_ivp_test(): solve_ivp() is used to solve the logistic ODE. Graphics saved as "solve_ivp_test.png" stats_test(): Test some scipy() statistics functions. [-2. -1. 0. 1. 3. 4. 6.] [5.39909665e-02 2.41970725e-01 3.98942280e-01 2.41970725e-01 4.43184841e-03 1.33830226e-04 6.07588285e-09] [0.02275013 0.15865525 0.5 0.84134475 0.9986501 0.99996833 1. ] [-1.28155157 -0.84162123 -0.52440051 -0.2533471 0. 0.2533471 0.52440051 0.84162123 1.28155157] [ 1.86641804 -0.39411223 0.03373532 -1.11893978 -1.2783859 ] scipy_test(): Normal end of execution. Thu Dec 26 10:24:09 2024