point all the image src URIs to use the fig/ path

And change the `<img>` tags to use Markdown's `![caption](/path/to/image)`
syntax.

01-numpy.md

@@ -124,7 +124,7 @@ we can print several things at once by separating them with commas.
 If we imagine the variable as a sticky note with a name written on it,
 assignment is like putting the sticky note on a particular value:
 
-<img src="img/python-sticky-note-variables-01.svg" alt="Variables as Sticky Notes" />
+![Variables as Sticky Notes](fig/python-sticky-note-variables-01.svg)
 
 This means that assigning a value to one variable does *not* change the values of other variables.
 For example,
@@ -138,7 +138,7 @@ print 'weight in kilograms:', weight_kg, 'and in pounds:', weight_lb
 weight in kilograms: 57.5 and in pounds: 126.5
 ~~~
 
-<img src="img/python-sticky-note-variables-02.svg" alt="Creating Another Variable" />
+![Creating Another Variable](fig/python-sticky-note-variables-02.svg)
 
 and then change `weight_kg`:
 
@@ -150,7 +150,7 @@ print 'weight in kilograms is now:', weight_kg, 'and weight in pounds is still:'
 weight in kilograms is now: 100.0 and weight in pounds is still: 126.5
 ~~~
 
-<img src="img/python-sticky-note-variables-03.svg" alt="Updating a Variable" />
+![Updating a Variable](fig/python-sticky-note-variables-03.svg)
 
 Since `weight_lb` doesn't "remember" where its value came from,
 it isn't automatically updated when `weight_kg` changes.
@@ -434,7 +434,7 @@ or the average for each day?
 As the diagram below shows,
 we want to perform the operation across an axis:
 
-<img src="img/python-operations-across-axes.svg" alt="Operations Across Axes" />
+![Operations Across Axes](fig/python-operations-across-axes.svg)
 
 To support this,
 most array methods allow us to specify the axis we want to work on.
@@ -500,7 +500,7 @@ pyplot.imshow(data)
 pyplot.show()
 ~~~
 
-<img src="../../novice/python/01-numpy_files/novice/python/01-numpy_71_0.png">
+![Heatmap of the Data](fig/01-numpy_71_0.png)
 
 Blue regions in this heat map are low values, while red shows high values.
 As we can see,
@@ -513,7 +513,7 @@ pyplot.plot(ave_inflammation)
 pyplot.show()
 ~~~
 
-<img src="../../novice/python/01-numpy_files/novice/python/01-numpy_73_0.png">
+![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`,
@@ -529,14 +529,14 @@ pyplot.plot(data.max(axis=0))
 pyplot.show()
 ~~~
 
-<img src="../../novice/python/01-numpy_files/novice/python/01-numpy_75_1.png">
+![Maximum Value Along The First Axis](fig/01-numpy_75_1.png)
 
 ~~~ {.python}
 pyplot.plot(data.min(axis=0))
 pyplot.show()
 ~~~
 
-<img src="../../novice/python/01-numpy_files/novice/python/01-numpy_75_3.png">
+![Minimum Value Along The First Axis](fig/01-numpy_75_3.png)
 
 The maximum value rises and falls perfectly smoothly,
 while the minimum seems to be a step function.
@@ -572,7 +572,7 @@ plt.tight_layout()
 plt.show()
 ~~~
 
-<img src="../../novice/python/01-numpy_files/novice/python/01-numpy_80_0.png">
+![The Previous Plots as Subplots](fig/01-numpy_80_0.png)
 
 The first two lines re-load our libraries as `np` and `plt`,
 which are the aliases most Python programmers use.

02-func.md

@@ -230,7 +230,7 @@ final = fahr_to_celsius(original)
 
 The diagram below shows what memory looks like after the first line has been executed:
 
-<img src="img/python-call-stack-01.svg" alt="Call Stack (Initial State)" />
+![Call Stack (Initial State)](fig/python-call-stack-01.svg)
 
 When we call `fahr_to_celsius`,
 Python *doesn't* create the variable `temp` right away.
@@ -240,12 +240,12 @@ to keep track of the variables defined by `fahr_to_kelvin`.
 Initially,
 this stack frame only holds the value of `temp`:
 
-<img src="img/python-call-stack-02.svg" alt="Call Stack Immediately After First Function Call" />
+![Call Stack Immediately After First Function Call](fig/python-call-stack-02.svg)
 
 When we call `fahr_to_kelvin` inside `fahr_to_celsius`,
 Python creates another stack frame to hold `fahr_to_kelvin`'s variables:
 
-<img src="img/python-call-stack-03.svg" alt="Call Stack During First Nested Function Call" />
+![Call Stack During First Nested Function Call](fig/python-call-stack-03.svg)
 
 It does this because there are now two variables in play called `temp`:
 the parameter to `fahr_to_celsius`,
@@ -258,26 +258,26 @@ When the call to `fahr_to_kelvin` returns a value,
 Python throws away `fahr_to_kelvin`'s stack frame
 and creates a new variable in the stack frame for `fahr_to_celsius` to hold the temperature in Kelvin:
 
-<img src="img/python-call-stack-04.svg" alt="Call Stack After Return From First Nested Function Call" />
+![Call Stack After Return From First Nested Function Call](fig/python-call-stack-04.svg)
 
 It then calls `kelvin_to_celsius`,
 which means it creates a stack frame to hold that function's variables:
 
-<img src="img/python-call-stack-05.svg" alt="Call Stack During Call to Second Nested Function" />
+![Call Stack During Call to Second Nested Function](fig/python-call-stack-05.svg)
 
 Once again,
 Python throws away that stack frame when `kelvin_to_celsius` is done
 and creates the variable `result` in the stack frame for `fahr_to_celsius`:
 
-<img src="img/python-call-stack-06.svg" alt="Call Stack After Second Nested Function Returns" />
+![Call Stack After Second Nested Function Returns](fig/python-call-stack-06.svg)
 
 Finally,
 when `fahr_to_celsius` is done,
 Python throws away *its* stack frame
 and puts its result in a new variable called `final`
 that lives in the stack frame we started with:
 
-<img src="img/python-call-stack-07.svg" alt="Call Stack After All Functions Have Finished" />
+![Call Stack After All Functions Have Finished](fig/python-call-stack-07.svg)
 
 This final stack frame is always there;
 it holds the variables we defined outside the functions in our code.

03-loop.md

@@ -46,15 +46,15 @@ def analyze(filename):
 analyze('inflammation-01.csv')
 ~~~
 
-<img src="../../novice/python/03-loop_files/novice/python/03-loop_2_0.png">
+![](fig/03-loop_2_0.png)
 
 We can use it to analyze other data sets one by one:
 
 ~~~ {.python}
 analyze('inflammation-02.csv')
 ~~~
 
-<img src="../../novice/python/03-loop_files/novice/python/03-loop_4_0.png">
+![](fig/03-loop_4_0.png)
 
 but we have a dozen data sets right now and more on the way.
 We want to create plots for all our data sets with a single statement.
@@ -344,18 +344,19 @@ for f in filenames:
 inflammation-01.csv
 ~~~
 
-<img src="../../novice/python/03-loop_files/novice/python/03-loop_49_1.png">
+![](fig/03-loop_49_1.png)
+
 ~~~ {.output}
 inflammation-02.csv
 ~~~
 
-<img src="../../novice/python/03-loop_files/novice/python/03-loop_49_3.png">
+![](fig/03-loop_49_3.png)
 
 ~~~ {.output}
 inflammation-03.csv
 ~~~
 
-<img src="../../novice/python/03-loop_files/novice/python/03-loop_49_5.png">
+![](fig/03-loop_49_5.png)
 
 Sure enough,
 the maxima of these data sets show exactly the same ramp as the first,

04-cond.md

@@ -73,7 +73,7 @@ RGB is an **additive color model**:
 every shade is some combination of red, green, and blue intensities.
 We can think of these three values as being the axes in a cube:
 
-<img src="img/color-cube.png" alt="RGB Color Cube" />
+![RGB Color Cube](fig/color-cube.png)
 
 An RGB color is an example of a multi-part value:
 like a Cartesian coordinate,
@@ -206,7 +206,7 @@ If the test is false,
 the body of the `else` is executed instead.
 Only one or the other is ever executed:
 
-<img src="img/python-flowchart-conditional.svg" alt="Executing a Conditional" />
+![Executing a Conditional](fig/python-flowchart-conditional.svg)
 
 Conditional statements don't have to include an `else`.
 If there isn't one,
@@ -333,7 +333,7 @@ As the diagram below shows,
 the **inner loop** runs from start to finish
 each time the **outer loop** runs once:
 
-<img src="img/python-flowchart-nested-loops.svg" alt="Execution of Nested Loops" />
+![Execution of Nested Loops](fig/python-flowchart-nested-loops.svg)
 
 We can combine nesting and conditionals to create patterns in an image:
 

05-defensive.md

@@ -239,7 +239,7 @@ The range of each time series is represented as a pair of numbers,
 which are the time the interval started and ended.
 The output is the largest range that they all include:
 
-<img src="img/python-overlapping-ranges.svg" alt="Overlapping Ranges" />
+![Overlapping Ranges](fig/python-overlapping-ranges.svg)
 
 Most novice programmers would solve this problem like this: