always use spaces after commas

episodes/02-numpy.md

@@ -218,7 +218,7 @@ It takes a bit of getting used to,
 but one way to remember the rule is that
 the index is how many steps we have to take from the start to get the item we want.
 
-![](fig/python-zero-index.svg){alt="'data' is a 3 by 3 numpy array containing row 0: \['A', 'B', 'C'\], row 1: \['D', 'E', 'F'\], androw 2: \['G', 'H', 'I'\]. Starting in the upper left hand corner, data\[0, 0\] = 'A', data\[0, 1\] = 'B',data\[0, 2\] = 'C', data\[1, 0\] = 'D', data\[1, 1\] = 'E', data\[1, 2\] = 'F', data\[2, 0\] = 'G',data\[2, 1\] = 'H', and data\[2, 2\] = 'I',in the bottom right hand corner."}
+![](fig/python-zero-index.svg){alt="'data' is a 3 by 3 numpy array containing row 0: \['A', 'B', 'C'\], row 1: \['D', 'E', 'F'\], androw 2: \['G', 'H', 'I'\]. Starting in the upper left hand corner, data\[0, 0\] = 'A', data\[0, 1\] = 'B',data\[0, 2\] = 'C', data\[1, 0\] = 'D', data\[1, 1\] = 'E', data\[1, 2\] = 'F', data\[2, 0\] = 'G',data\[2, 1\] = 'H', and data\[2, 2\] = 'I', in the bottom right hand corner."}
 
 :::::::::::::::::::::::::::::::::::::::::  callout
 
@@ -585,7 +585,7 @@ using NumPy's `vstack` and `hstack` functions for vertical and horizontal stacki
 ```python
 import numpy
 
-A = numpy.array([[1,2,3], [4,5,6], [7, 8, 9]])
+A = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
 print('A = ')
 print(A)
 

episodes/03-matplotlib.md

@@ -202,7 +202,7 @@ If we want to change this, we can use the `set_ylim(min, max)` method of each 'a
 for example:
 
 ```python
-axes3.set_ylim(0,6)
+axes3.set_ylim(0, 6)
 ```
 
 Update your plotting code to automatically set a more appropriate scale.
@@ -216,7 +216,7 @@ Update your plotting code to automatically set a more appropriate scale.
 # One method
 axes3.set_ylabel('min')
 axes3.plot(numpy.amin(data, axis=0))
-axes3.set_ylim(0,6)
+axes3.set_ylim(0, 6)
 ```
 
 :::::::::::::::::::::::::

episodes/04-lists.md

@@ -506,7 +506,7 @@ print(repeats)
 
 1. `[2, 4, 6, 8, 10, 2, 4, 6, 8, 10]`
 2. `[4, 8, 12, 16, 20]`
-3. `[[2, 4, 6, 8, 10],[2, 4, 6, 8, 10]]`
+3. `[[2, 4, 6, 8, 10], [2, 4, 6, 8, 10]]`
 4. `[2, 4, 6, 8, 10, 4, 8, 12, 16, 20]`
 
 The technical term for this is *operator overloading*:

episodes/06-files.md

@@ -87,19 +87,19 @@ for filename in filenames:
 inflammation-01.csv
 ```
 
-![](fig/03-loop_49_1.png){alt='Output from the first iteration of the for loop. Three line graphs showing the daily average,maximum and minimum inflammation over a 40-day period for all patients in the first dataset.'}
+![](fig/03-loop_49_1.png){alt='Output from the first iteration of the for loop. Three line graphs showing the daily average, maximum and minimum inflammation over a 40-day period for all patients in the first dataset.'}
 
 ```output
 inflammation-02.csv
 ```
 
-![](fig/03-loop_49_3.png){alt='Output from the second iteration of the for loop. Three line graphs showing the daily average,maximum and minimum inflammation over a 40-day period for all patients in the seconddataset.'}
+![](fig/03-loop_49_3.png){alt='Output from the second iteration of the for loop. Three line graphs showing the daily average, maximum and minimum inflammation over a 40-day period for all patients in the seconddataset.'}
 
 ```output
 inflammation-03.csv
 ```
 
-![](fig/03-loop_49_5.png){alt='Output from the third iteration of the for loop. Three line graphs showing the daily average,maximum and minimum inflammation over a 40-day period for all patients in the thirddataset.'}
+![](fig/03-loop_49_5.png){alt='Output from the third iteration of the for loop. Three line graphs showing the daily average, maximum and minimum inflammation over a 40-day period for all patients in the thirddataset.'}
 
 The plots generated for the second clinical trial file look very similar to the plots for
 the first file: their average plots show similar "noisy" rises and falls; their maxima plots
@@ -161,7 +161,7 @@ Use each of the files once to generate a dataset containing values averaged over
 
 ```python
 filenames = glob.glob('inflammation*.csv')
-composite_data = numpy.zeros((60,40))
+composite_data = numpy.zeros((60, 40))
 for filename in filenames:
     # sum each new file's data into composite_data as it's read
     #
@@ -181,7 +181,7 @@ import numpy
 import matplotlib.pyplot
 
 filenames = glob.glob('inflammation*.csv')
-composite_data = numpy.zeros((60,40))
+composite_data = numpy.zeros((60, 40))
 
 for filename in filenames:
     data = numpy.loadtxt(fname = filename, delimiter=',')

episodes/08-func.md

@@ -286,7 +286,7 @@ let's use NumPy to create a matrix of 0's
 and then offset its values to have a mean value of 3:
 
 ```python
-z = numpy.zeros((2,2))
+z = numpy.zeros((2, 2))
 print(offset_mean(z, 3))
 ```
 

episodes/10-defensive.md

@@ -264,7 +264,7 @@ 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:
 
-![](fig/python-overlapping-ranges.svg){alt='Graph showing three number lines and, at the bottom,the interval that they overlap.'}
+![](fig/python-overlapping-ranges.svg){alt='Graph showing three number lines and, at the bottom, the interval that they overlap.'}
 
 Most novice programmers would solve this problem like this:
 

episodes/12-cmdline.md

@@ -994,7 +994,7 @@ def main():
 
 def count_file(filename):
     """count the number of lines in a file"""
-    f = open(filename,'r')
+    f = open(filename, 'r')
     nlines = len(f.readlines())
     f.close()
     return(nlines)