Shorten lines below 100 characters

Author: mike-ivs

_episodes/03-matplotlib.md

@@ -13,8 +13,8 @@ keypoints:
 ---
 
 ## Visualizing data
-The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers," and
-the best way to develop insight is often to visualize data.  Visualization deserves an entire
+The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers,"
+and the best way to develop insight is often to visualize data.  Visualization deserves an entire
 lecture of its own, but we can explore a few features of Python's `matplotlib` library here.  While
 there is no official plotting library, `matplotlib` is the _de facto_ standard.  First, we will
 import the `pyplot` module from `matplotlib` and use two of its functions to create and display a
@@ -31,11 +31,12 @@ matplotlib.pyplot.show()
 from blue to yellow.](../fig/inflammation-01-imshow.svg)
 
 Each row in the heat map corresponds to a patient in the clinical trial dataset, and each column
-corresponds to a day in the dataset.  Blue pixels in this heat map represent low values, while yellow
-pixels represent high values.  As we can see, the general number of inflammation flare-ups for the patients
-rises and falls over a 40-day period.
+corresponds to a day in the dataset.  Blue pixels in this heat map represent low values, while
+yellow pixels represent high values.  As we can see, the general number of inflammation flare-ups
+for the patients rises and falls over a 40-day period.
 
-So far so good as this is in line with our knowledge of the clinical trial and Dr. Maverick's claims:
+So far so good as this is in line with our knowledge of the clinical trial and Dr. Maverick's
+claims:
 
 * the patients take their medication once their inflammation flare-ups begin
 * it takes around 3 weeks for the medication to take effect and begin reducing flare-ups
@@ -54,8 +55,8 @@ matplotlib.pyplot.show()
 
 Here, we have put the average inflammation per day across all patients in the variable
 `ave_inflammation`, then asked `matplotlib.pyplot` to create and display a line graph of those
-values.  The result is a reasonably linear rise and fall, in line with Dr. Maverick's claim that the
-medication takes 3 weeks to take effect.  But a good data scientist doesn't just consider the
+values.  The result is a reasonably linear rise and fall, in line with Dr. Maverick's claim that
+the medication takes 3 weeks to take effect.  But a good data scientist doesn't just consider the
 average of a dataset, so let's have a look at two other statistics:
 
 ~~~
@@ -84,8 +85,8 @@ You can group similar plots in a single figure using subplots.
 This script below uses a number of new commands. The function `matplotlib.pyplot.figure()`
 creates a space into which we will place all of our plots. The parameter `figsize`
 tells Python how big to make this space. Each subplot is placed into the figure using
-its `add_subplot` [method]({{ page.root }}/reference.html#method). The `add_subplot` method takes 3
-parameters. The first denotes how many total rows of subplots there are, the second parameter
+its `add_subplot` [method]({{ page.root }}/reference.html#method). The `add_subplot` method takes
+3 parameters. The first denotes how many total rows of subplots there are, the second parameter
 refers to the total number of subplot columns, and the final parameter denotes which subplot
 your variable is referencing (left-to-right, top-to-bottom). Each subplot is stored in a
 different variable (`axes1`, `axes2`, `axes3`). Once a subplot is created, the axes can

_episodes/05-loop.md

@@ -19,7 +19,8 @@ In the episode about visualizing data,
 we wrote Python code that plots values of interest from our first
 inflammation dataset (`inflammation-01.csv`), which revealed some suspicious features in it.
 
-![Line graphs showing average, maximum and minimum inflammation across all patients over a 40-day period.](../fig/03-loop_2_0.png)
+![Line graphs showing average, maximum and minimum inflammation across all patients over a 40-day
+period.](../fig/03-loop_2_0.png)
 
 We have a dozen data sets right now and potentially more on the way if Dr. Maverick
 can keep up their surprisingly fast clinical trial rate. We want to create plots for all of
@@ -149,7 +150,8 @@ for variable in collection:
 
 Using the odds example above, the loop might look like this:
 
-![Loop variable 'num' being assigned the value of each element in the list `odds` in turn and then being printed](../fig/05-loops_image_num.png)
+![Loop variable 'num' being assigned the value of each element in the list `odds` in turn and
+then being printed](../fig/05-loops_image_num.png)
 
 where each number (`num`) in the variable `odds` is looped through and printed one number after
 another. The other numbers in the diagram denote which loop cycle the number was printed in (1

_episodes/06-files.md

@@ -120,7 +120,8 @@ consistently zero across every day of the trial. If we produce a heat map for th
 we see the following:
 
 ![Heat map of the third inflammation dataset. Note that there are sporadic zero values throughout
-the entire dataset, and the last patient only has zero values over the 40 day study.](../fig/inflammation-03-imshow.svg)
+the entire dataset, and the last patient only has zero values over the 40 day study.
+](../fig/inflammation-03-imshow.svg)
 
 We can see that there are zero values sporadically distributed across all patients and days of the
 clinical trial, suggesting that there were potential issues with data collection throughout the