### Added matplotlib.pyplot and matplotlib inline

parent 532249fb
 ... ... @@ -505,19 +505,31 @@ matplotlib.pyplot.show(image) Blue regions in this heat map are low values, while red shows high values. As we can see, inflammation rises and falls over a 40-day period. > ## Some IPython magic {.callout} > > If you're using an IPython / Jupyter notebook, > you'll need to execute the following command > in order for your matplotlib images to appear > when `show()` is called: > `% matplotlib inline`. > The `%` indicates an IPython magic function - > a function that is only valid within the notebook environment. > Note that you only have to execute this function once per notebook. Let's take a look at the average inflammation over time: ~~~ {.python} ave_inflammation = data.mean(axis=0) ave_plot = pyplot.plot(ave_inflammation) pyplot.show(ave_plot) ave_plot = matplotlib.pyplot.plot(ave_inflammation) matplotlib.pyplot.show(ave_plot) ~~~ ![Average Inflammation Over Time](fig/01-numpy_73_0.png) Here, we have put the average per day across all patients in the variable `ave_inflammation`, then asked `pyplot` to create and display a line graph of those values. then asked `matplotlib.pyplot` to create and display a line graph of those values. The result is roughly a linear rise and fall, which is suspicious: based on other studies, ... ... @@ -525,15 +537,15 @@ we expect a sharper rise and slower fall. Let's have a look at two other statistics: ~~~ {.python} max_plot = pyplot.plot(data.max(axis=0)) pyplot.show(max_plot) max_plot = matplotlib.pyplot.plot(data.max(axis=0)) matplotlib.pyplot.show(max_plot) ~~~ ![Maximum Value Along The First Axis](fig/01-numpy_75_1.png) ~~~ {.python} min_plot = pyplot.plot(data.min(axis=0)) pyplot.show(min_plot) min_plot = matplotlib.pyplot.plot(data.min(axis=0)) matplotlib.pyplot.show(min_plot) ~~~ ![Minimum Value Along The First Axis](fig/01-numpy_75_3.png) ... ...
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