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# elegant-scipy / elegant-scipy

Fix various markup issues
stefanv committed May 9, 2017
1 parent 2747c5c commit 17daa629c65ea6bcf50aaf02bb593a9b5b316de7
Showing with 9 additions and 18 deletions.
1. +0 −8 CONTRIBUTING.md
2. +2 −2 markdown/ch6.markdown
3. +3 −3 markdown/ch7.markdown
4. +3 −4 markdown/ch8.markdown
5. +1 −1 markdown/epilogue.markdown
 @@ -2,14 +2,6 @@ ## Markup quirks - Lines cannot start with "\$", e.g. ``` \$X\$ is a matrix ``` won't compile, so move the "\$X" to the end of the previous line. - Lines cannot have trailing spaces - Do not combine markdown images, i.e., `![caption](image.png)`,
 @@ -194,7 +194,7 @@ How do you draw nodes and edges in such a way that you don't get a complete mess such as this one? One way is to put nodes that share many edges close together. It turns out @@ -1085,5 +1085,5 @@ np.corrcoef([pagerank, pagerank_power, pagerank_power2]) ## Concluding remarks The field of linear algebra is far too broad to adequately cover in a chapter, but we hope that we have given you a taste of the power of it here, and of but this chapter gave you a glimpse into its power, and of the way Python, NumPy, and SciPy make its elegant algorithms accessible.
 @@ -652,9 +652,9 @@ different images. When the images are perfectly aligned, any object of uniform color will create a large correlation between the shades of the different component channels, and a correspondingly large NMI value. In a sense, NMI measures how easy it would be to predict a pixel value of one image given the value of the corresponding pixel in the other. It was defined in the paper: Studholme, C., Hill, D.L.G., Hawkes, D.J.: An Overlap Invariant Entropy Measure of 3D Medical Image Alignment. Patt. Rec. 32, 71–86 (1999): value of the corresponding pixel in the other. It was defined in the paper "Studholme, C., Hill, D.L.G., Hawkes, D.J., *An Overlap Invariant Entropy Measure of 3D Medical Image Alignment*, Patt. Rec. 32, 71–86 (1999)": \$\$I(X, Y) = \frac{H(X) + H(Y)}{H(X, Y)},\$\$
 @@ -330,7 +330,7 @@ def is_sequence(line): def reads_to_kmers(reads_iter, k=7): for read in reads_iter: for start in range(0, len(read) - k): yield read[start : start + k] # note yeild, so this is a generator yield read[start : start + k] # note yield, so this is a generator def kmer_counter(kmer_iter): counts = {} @@ -875,6 +875,5 @@ analysis, think about whether you can do it streaming. If you can, just do it from the beginning. Your future self will thank you. Doing it later is harder, and results in things like this: ![TODOs in history. Comic by Manu Cornet, used with permission](https://pbs.twimg.com/media/CDxc7HTVIAAsiFO.jpg)
 @@ -6,7 +6,7 @@ Our main goal with this book was to promote elegant uses of the NumPy and SciPy libraries. While teaching you how to do effective scientific analysis with SciPy, We hope to have inspired in you the feeling that quality code is something we hope to have inspired in you the feeling that quality code is something worth striving for. ## Where to next?