Additional suggestions from code review

Author: JeremyPike

episodes/07-thresholding.md

@@ -352,8 +352,7 @@ Thus, this image is a good candidate for thresholding with Otsu's method.
 The mathematical details of how this works are complicated (see
 [the scikit-image documentation](https://scikit-image.org/docs/dev/api/skimage.filters.html#threshold-otsu)
 if you are interested),
-but the outcome is that Otsu's method finds a threshold value between
-the two peaks of a grayscale histogram.
+but the outcome is that Otsu's method finds a threshold value between the two peaks of a grayscale histogram which might correspond well to the foreground and background depending the data and application.
 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
 The histogram of the maize root image may prompt questions from learners about the interpretation of the peaks and the broader region around 0.6. The focus here is on the separation of background and foreground pixel values. We note that Otsu's method does not work well for the image with the shapes used earlier in this episode, as the foreground pixel values are more distributed. These examples could be augmented by a discussion of unimodal, bimodal, and multimodal histograms. While these points can lead to fruitful discussions, the text in this episode attempts to reduce cognitive load and deliberately simplifies the discussion.
 ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
@@ -633,7 +632,7 @@ def enhanced_root_mass(filename, sigma):
     t = ski.filters.threshold_otsu(blurred_image[binary_mask])
 
     # update binary mask to identify pixels which are both less than 0.95 and greater than t
-    binary_mask = np.logical_and(binary_mask, blurred_image > t)
+    binary_mask = binary_mask & (blurred_image > t)
 
     # determine root mass ratio
     root_pixels = np.count_nonzero(binary_mask)