def example_plot_contour(): # inspired by http://stackoverflow.com/questions/10291221/axis-limits-for-scatter-plot-not-holding-in-matplotlib # random data x = np.random.randn(50) y = np.random.randn(100) X, Y = np.meshgrid(y, x) Z1 = plt.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) Z2 = plt.bivariate_normal(X, Y, 1.5, 0.5, 1, 1) Z = 10 * (Z1 - Z2) plot_contour(Z,x,y,title='example_plot_contour',xtitle='x-stuff',ytitle='y-stuff',plot_points=1,show=1)
def example_plot_contour(): # inspired by http://stackoverflow.com/questions/10291221/axis-limits-for-scatter-plot-not-holding-in-matplotlib # random data x = np.random.randn(50) y = np.random.randn(100) X, Y = np.meshgrid(y, x) Z1 = plt.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) Z2 = plt.bivariate_normal(X, Y, 1.5, 0.5, 1, 1) Z = 10 * (Z1 - Z2) plot_contour(Z, x, y, title='example_plot_contour', xtitle='x-stuff', ytitle='y-stuff', plot_points=1, show=1)
sim_fix_count = 500 ##### STEP B. ##### # Create 2D Gaussian Mask template as a 2D numpy array # # Create x and y pixel ranges for Gauss Mask. # x = np.arange(-sigma_x * 2.5, sigma_x * 2.5, 1) y = np.arange(-sigma_y * 2.5, sigma_y * 2.5, 1) # Create X and Y pixel position values for each element of Gauss. Mask. # X, Y = np.meshgrid(x, y) # Create 2D Gauss Mask as numpy array using X and Y mesh grid data # and sigma's, with Gauss centered in 2D array (0,0) # gauss = plb.bivariate_normal(X, Y, sigma_x, sigma_y, 0, 0) # Normalize the Gausian, such that the max value in the is 1.0. # gauss *= 1.0 / gauss.flatten().max() ghw, ghh = gauss.shape[0] // 2, gauss.shape[1] // 2 ##### STEP C. ##### # Load Background Image Displayed During Eye Data Collection # Flip vertically # image_array = np.flipud(mpimg.imread("./images/canal.jpg")) # Get background image size # image_size = image_array.shape #(image_array.shape[0],image_array.shape[1]) ihw, ihh = image_size[0] / 2, image_size[1] / 2
sim_fix_count=500 ##### STEP B. ##### # Create 2D Gaussian Mask template as a 2D numpy array # # Create x and y pixel ranges for Gauss Mask. # x = np.arange(-sigma_x*2.5,sigma_x*2.5, 1) y = np.arange(-sigma_y*2.5, sigma_y*2.5, 1) # Create X and Y pixel position values for each element of Gauss. Mask. # X, Y = np.meshgrid(x, y) # Create 2D Gauss Mask as numpy array using X and Y mesh grid data # and sigma's, with Gauss centered in 2D array (0,0) # gauss=plb.bivariate_normal(X, Y, sigma_x, sigma_y, 0,0) # Normalize the Gausian, such that the max value in the is 1.0. # gauss*=1.0/gauss.flatten().max() ghw,ghh=gauss.shape[0]//2,gauss.shape[1]//2 ##### STEP C. ##### # Load Background Image Displayed During Eye Data Collection # Flip vertically # image_array=np.flipud(mpimg.imread("./images/canal.jpg")) # Get background image size # image_size=image_array.shape#(image_array.shape[0],image_array.shape[1]) ihw,ihh=image_size[0]/2,image_size[1]/2