transparency blend example

Author: choldgraf

examples/images_contours_and_fields/image_transparency_blend.py

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+"""
+=============================================
+Blend transparency with color with 2-D images
+=============================================
+
+Blend transparency with color to highlight parts of data with imshow.
+
+A common use for :func:`matplotlib.pyplot.imshow` is to plot a 2-D statistical
+map. In this case, it's common to visualize the statistic of choice (e.g.,
+a t-statistic) alongisde another value of interest (e.g., the p-value for that
+statistic). One way to do this is to map the p-value onto the transparency of
+the image such that data with "significant" values are highlighted.
+
+This example demonstrates how you can achieve this effect using
+:class:`matplotlib.colors.Normalize. Note that it is not possible to directly
+pass alpha values to :func:`matplotlib.pyplot.imshow`.
+
+First we will generate some data, in this case, we'll create two 2-D "blobs"
+in a 2-D grid. One blob will be positive, and the other negative.
+"""
+# sphinx_gallery_thumbnail_number = 2
+import numpy as np
+from scipy.stats import multivariate_normal
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+
+# 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)
+
+# Generate the blobs
+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
+
+# We'll plot these blobs using only ``imshow``.
+vmax = np.abs(weights).max() * 1.5
+vmin = -vmax
+cmap = plt.cm.RdYlBu
+fig, ax = plt.subplots()
+ax.imshow(weights, extent=(xmin, xmax, ymin, ymax), cmap=cmap)
+ax.set_axis_off()
+
+################################################################################
+# Blending in transparency
+# ========================
+#
+# Below, we'll recreate the same plot, but this time we'll blend in
+# transparency with the image so that the extreme values are highlighted.
+# We'll also add in contour lines to highlight the image values.
+
+# Create an alpha channel based on weight values
+alphas = Normalize(0, .0001, clip=True)(np.abs(weights))
+alphas = np.clip(alphas, .4, 1)
+
+# Normalize the colors b/w 0 and 1, we'll then pass an MxNx4 array to imshow
+colors = Normalize(vmin, vmax)(weights)
+colors = cmap(colors)
+
+# Now set the alpha channel to the one we created above
+colors[..., -1] = alphas
+
+# Create the figure and image
+# Note that the absolute values may be slightly different
+fig, ax = plt.subplots()
+ax.imshow(colors, extent=(xmin, xmax, ymin, ymax))
+
+# Add contour lines to further highlight different levels.
+ax.contour(weights[::-1], levels=[-.0001, .0001], colors='k', linestyles='-')
+ax.set_axis_off()
+plt.show()