no scipy

choldgraf · choldgraf · commit 4b7f4a51093a · 2017-10-14T21:33:53.000-07:00
diff --git a/examples/images_contours_and_fields/image_transparency_blend.py b/examples/images_contours_and_fields/image_transparency_blend.py
@@ -18,24 +18,33 @@
 """
 # sphinx_gallery_thumbnail_number = 3
 import numpy as np
-from scipy.stats import multivariate_normal
 import matplotlib.pyplot as plt
 from matplotlib.colors import Normalize
+from numpy.random import multivariate_normal
 
 # Generate the space in which the blobs will live
 xmin, xmax, ymin, ymax = (0, 100, 0, 100)
-xx = np.linspace(xmin, xmax, 100)
-yy = np.linspace(ymin, ymax, 100)
-grid = np.array(np.meshgrid(xx, yy))
-grid = grid.transpose(2, 1, 0)
+n_bins = 100
+xx = np.linspace(xmin, xmax, n_bins)
+yy = np.linspace(ymin, ymax, n_bins)
 
 # Generate the blobs. The range of the values is roughly -.0002 to .0002
 means_high = [20, 50]
 means_low = [50, 60]
-cov = [[500, 0], [0, 800]]
-gauss_high = multivariate_normal.pdf(grid, means_high, cov)
-gauss_low = -1 * multivariate_normal.pdf(grid, means_low, cov)
-weights = gauss_high + gauss_low
+var = [150, 200]
+
+def normal_pdf(x, mean, var):
+    return np.exp(-(x - mean)**2 / (2*var))
+
+gauss_x_high = normal_pdf(xx, means_high[0], var[0])
+gauss_y_high = normal_pdf(yy, means_high[1], var[0])
+
+gauss_x_low = normal_pdf(xx, means_low[0], var[1])
+gauss_y_low = normal_pdf(yy, means_low[1], var[1])
+
+weights_high = np.array(np.meshgrid(gauss_x_high, gauss_y_high)).prod(0)
+weights_low = -1 * np.array(np.meshgrid(gauss_x_low, gauss_y_low)).prod(0)
+weights = weights_high + weights_low
 
 # We'll also create a grey background into which the pixels will fade
 greys = np.ones(weights.shape + (3,)) * 70
@@ -89,7 +98,7 @@
 
 # Create an alpha channel based on weight values
 # Any value whose absolute value is > .0001 will have zero transparency
-alphas = Normalize(0, .0001, clip=True)(np.abs(weights))
+alphas = Normalize(0, .3, clip=True)(np.abs(weights))
 alphas = np.clip(alphas, .4, 1)  # alpha value clipped at the bottom at .4
 
 # Normalize the colors b/w 0 and 1, we'll then pass an MxNx4 array to imshow
@@ -106,6 +115,10 @@
 ax.imshow(colors, extent=(xmin, xmax, ymin, ymax))
 
 # Add contour lines to further highlight different levels.
+ax.contour(weights[::-1], levels=[-.1, .1], colors='k', linestyles='-')
+ax.set_axis_off()
+plt.show()
+
 ax.contour(weights[::-1], levels=[-.0001, .0001], colors='k', linestyles='-')
 ax.set_axis_off()
 plt.show()
