02-numpy.md: remove visualization content

_episodes/02-numpy.md

 ---
 title: Analyzing Patient Data
 teaching: 60
-exercises: 30
+exercises: 20
 questions:
 - "How can I process tabular data files in Python?"
 objectives:
@@ -10,7 +10,6 @@ objectives:
 - "Read tabular data from a file into a program."
 - "Select individual values and subsections from data."
 - "Perform operations on arrays of data."
-- "Plot simple graphs from data."
 keypoints:
 - "Import a library into a program using `import libraryname`."
 - "Use the `numpy` library to work with arrays in Python."
@@ -18,11 +17,9 @@ keypoints:
 - "Use `array[x, y]` to select a single element from a 2D array."
 - "Array indices start at 0, not 1."
 - "Use `low:high` to specify a `slice` that includes the indices from `low` to `high-1`."
-- "All the indexing and slicing that works on arrays also works on strings."
 - "Use `# some kind of explanation` to add comments to programs."
 - "Use `numpy.mean(array)`, `numpy.max(array)`, and `numpy.min(array)` to calculate simple statistics."
 - "Use `numpy.mean(array, axis=0)` or `numpy.mean(array, axis=1)` to calculate statistics across the specified axis."
-- "Use the `pyplot` library from `matplotlib` for creating simple visualizations."
 ---
 
 Words are useful, but what's more useful are the sentences and stories we build with them.
@@ -391,9 +388,9 @@ standard deviation: 4.61383319712
 > to see a list of all functions and attributes that you can use. After selecting one, you
 > can also add a question mark (e.g. `numpy.cumprod?`), and IPython will return an
 > explanation of the method! This is the same as doing `help(numpy.cumprod)`.
-> Similarly, if you are using the "plain vanilla" Python interpreter, you can type `numpy.` 
-> and press the <kbd>Tab</kbd> key twice for a listing of what is available. You can then use the 
-> `help()` function to see an explanation of the function you're interested in, 
+> Similarly, if you are using the "plain vanilla" Python interpreter, you can type `numpy.`
+> and press the <kbd>Tab</kbd> key twice for a listing of what is available. You can then use the
+> `help()` function to see an explanation of the function you're interested in,
 > for example: `help(numpy.cumprod)`.
 {: .callout}
 
@@ -496,112 +493,6 @@ print(numpy.mean(data, axis=1))
 
 which is the average inflammation per patient across all days.
 
-## 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
-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
-heat map of our data:
-
-~~~
-import matplotlib.pyplot
-image = matplotlib.pyplot.imshow(data)
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Heatmap of the Data](../fig/inflammation-01-imshow.svg)
-
-Blue pixels in this heat map represent low values, while yellow pixels represent high values.  As we
-can see, inflammation rises and falls over a 40-day period.  Let's take a look at the average inflammation over time:
-
-~~~
-ave_inflammation = numpy.mean(data, axis=0)
-ave_plot = matplotlib.pyplot.plot(ave_inflammation)
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Average Inflammation Over Time](../fig/inflammation-01-average.svg)
-
-Here, we have put the average 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
-roughly linear rise and fall, which is suspicious: we might instead expect a sharper rise and slower
-fall.  Let's have a look at two other statistics:
-
-~~~
-max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Maximum Value Along The First Axis](../fig/inflammation-01-maximum.svg)
-
-~~~
-min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Minimum Value Along The First Axis](../fig/inflammation-01-minimum.svg)
-
-The maximum value rises and falls smoothly, while the minimum seems to be a step function.  Neither
-trend seems particularly likely, so either there's a mistake in our calculations or something is
-wrong with our data.  This insight would have been difficult to reach by examining the numbers
-themselves without visualization tools.
-
-### Grouping plots
-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/#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
-be titled using the `set_xlabel()` command (or `set_ylabel()`).
-Here are our three plots side by side:
-
-~~~
-import numpy
-import matplotlib.pyplot
-
-data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-
-fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
-
-axes1 = fig.add_subplot(1, 3, 1)
-axes2 = fig.add_subplot(1, 3, 2)
-axes3 = fig.add_subplot(1, 3, 3)
-
-axes1.set_ylabel('average')
-axes1.plot(numpy.mean(data, axis=0))
-
-axes2.set_ylabel('max')
-axes2.plot(numpy.max(data, axis=0))
-
-axes3.set_ylabel('min')
-axes3.plot(numpy.min(data, axis=0))
-
-fig.tight_layout()
-
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![The Previous Plots as Subplots](../fig/inflammation-01-group-plot.svg)
-
-The [call]({{ page.root }}/reference/#function-call) to `loadtxt` reads our data,
-and the rest of the program tells the plotting library
-how large we want the figure to be,
-that we're creating three subplots,
-what to draw for each one,
-and that we want a tight layout.
-(If we leave out that call to `fig.tight_layout()`,
-the graphs will actually be squeezed together more closely.)
-
 
 > ## Slicing Strings
 >
@@ -671,140 +562,6 @@ the graphs will actually be squeezed together more closely.)
 > {: .solution}
 {: .challenge}
 
-> ## Plot Scaling
->
-> Why do all of our plots stop just short of the upper end of our graph?
->
-> > ## Solution
-> > Because matplotlib normally sets x and y axes limits to the min and max of our data
-> > (depending on data range)
-> {: .solution}
->
-> If we want to change this, we can use the `set_ylim(min, max)` method of each 'axes',
-> for example:
->
-> ~~~
-> axes3.set_ylim(0,6)
-> ~~~
-> {: .language-python}
->
-> Update your plotting code to automatically set a more appropriate scale.
-> (Hint: you can make use of the `max` and `min` methods to help.)
->
-> > ## Solution
-> > ~~~
-> > # One method
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
-> > axes3.set_ylim(0,6)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
->
-> > ## Solution
-> > ~~~
-> > # A more automated approach
-> > min_data = numpy.min(data, axis=0)
-> > axes3.set_ylabel('min')
-> > axes3.plot(min_data)
-> > axes3.set_ylim(numpy.min(min_data), numpy.max(min_data) * 1.1)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Drawing Straight Lines
->
-> In the center and right subplots above, we expect all lines to look like step functions because
-> non-integer value are not realistic for the minimum and maximum values. However, you can see
-> that the lines are not always vertical or horizontal, and in particular the step function
-> in the subplot on the right looks slanted. Why is this?
->
-> > ## Solution
-> > Because matplotlib interpolates (draws a straight line) between the points.
-> > One way to do avoid this is to use the Matplotlib `drawstyle` option:
-> >
-> > ~~~
-> > import numpy
-> > import matplotlib.pyplot
-> >
-> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-> >
-> > fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
-> >
-> > axes1 = fig.add_subplot(1, 3, 1)
-> > axes2 = fig.add_subplot(1, 3, 2)
-> > axes3 = fig.add_subplot(1, 3, 3)
-> >
-> > axes1.set_ylabel('average')
-> > axes1.plot(numpy.mean(data, axis=0), drawstyle='steps-mid')
-> >
-> > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0), drawstyle='steps-mid')
-> >
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0), drawstyle='steps-mid')
-> >
-> > fig.tight_layout()
-> >
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> ![Plot with step lines](../fig/inflammation-01-line-styles.svg)
-> {: .solution}
-{: .challenge}
-
-> ## Make Your Own Plot
->
-> Create a plot showing the standard deviation (`numpy.std`)
-> of the inflammation data for each day across all patients.
->
-> > ## Solution
-> > ~~~
-> > std_plot = matplotlib.pyplot.plot(numpy.std(data, axis=0))
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Moving Plots Around
->
-> Modify the program to display the three plots on top of one another
-> instead of side by side.
->
-> > ## Solution
-> > ~~~
-> > import numpy
-> > import matplotlib.pyplot
-> >
-> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-> >
-> > # change figsize (swap width and height)
-> > fig = matplotlib.pyplot.figure(figsize=(3.0, 10.0))
-> >
-> > # change add_subplot (swap first two parameters)
-> > axes1 = fig.add_subplot(3, 1, 1)
-> > axes2 = fig.add_subplot(3, 1, 2)
-> > axes3 = fig.add_subplot(3, 1, 3)
-> >
-> > axes1.set_ylabel('average')
-> > axes1.plot(numpy.mean(data, axis=0))
-> >
-> > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0))
-> >
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
-> >
-> > fig.tight_layout()
-> >
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
 > ## Stacking Arrays
 >
 > Arrays can be concatenated and stacked on top of one another,