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Fix various markup issues

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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?

<img src="https://upload.wikimedia.org/wikipedia/commons/9/90/Visualization_of_wiki_structure_using_prefuse_visualization_package.png"/>
<!-- caption text="Visualization of wikipedia structure. Created by Chris Davis. [CC-BY-SA-3.0](https://commons.wikimedia.org/wiki/GNU_Free_Documentation_License)." -->
<!-- caption text="Visualization of wikipedia structure. Created by Chris Davis and released under CC-BY-SA-3.0 (https://commons.wikimedia.org/wiki/GNU_Free_Documentation_License)." -->


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:
<img src="https://pbs.twimg.com/media/CDxc6HTVIAAsiFO.jpg" alt="TODO"/>
<!-- caption text="TODOs in history. Comic by Manu Cornet, used with
permission" -->
![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?

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