Merge pull request #995 from henryiii/henryiii/fix/anumpy

fix: use amin/amax from numpy

Hi @henryiii ,

Thank you for the pull request. Since numpy.min and numpy.max don't actually exist on the official documentation so it would also make sense to change them to numpy.amin and numpy.amax in the lesson.

_episodes/02-numpy.md

@@ -17,7 +17,8 @@ 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`."
-- "Use `numpy.mean(array)`, `numpy.max(array)`, and `numpy.min(array)` to calculate simple statistics."
+- "Use `# some kind of explanation` to add comments to programs."
+- "Use `numpy.mean(array)`, `numpy.amax(array)`, and `numpy.amin(array)` to calculate simple statistics."
 - "Use `numpy.mean(array, axis=0)` or `numpy.mean(array, axis=1)` to calculate statistics across the specified axis."
 ---
 
@@ -357,16 +358,16 @@ We'll also use multiple assignment,
 a convenient Python feature that will enable us to do this all in one line.
 
 ~~~
-maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)
+maxval, minval, stdval = numpy.amax(data), numpy.amin(data), numpy.std(data)
 
 print('maximum inflammation:', maxval)
 print('minimum inflammation:', minval)
 print('standard deviation:', stdval)
 ~~~
 {: .language-python}
 
-Here we've assigned the return value from `numpy.max(data)` to the variable `maxval`, the value
-from `numpy.min(data)` to `minval`, and so on.
+Here we've assigned the return value from `numpy.amax(data)` to the variable `maxval`, the value
+from `numpy.amin(data)` to `minval`, and so on.
 
 ~~~
 maximum inflammation: 20.0
@@ -400,7 +401,7 @@ then ask it to do the calculation:
 
 ~~~
 patient_0 = data[0, :] # 0 on the first axis (rows), everything on the second (columns)
-print('maximum inflammation for patient 0:', numpy.max(patient_0))
+print('maximum inflammation for patient 0:', numpy.amax(patient_0))
 ~~~
 {: .language-python}
 
@@ -413,7 +414,7 @@ We don't actually need to store the row in a variable of its own.
 Instead, we can combine the selection and the function call:
 
 ~~~
-print('maximum inflammation for patient 2:', numpy.max(data[2, :]))
+print('maximum inflammation for patient 2:', numpy.amax(data[2, :]))
 ~~~
 {: .language-python}
 
@@ -428,7 +429,7 @@ diagram on the right)? As the diagram below shows, we want to perform the
 operation across an axis:
 
 ![Per-patient maximum inflammation is computed row-wise across all columns using
-numpy.max(data, axis=1). Per-day average inflammation is computed column-wise across all rows using
+numpy.amax(data, axis=1). Per-day average inflammation is computed column-wise across all rows using
 numpy.mean(data, axis=0).](../fig/python-operations-across-axes.png)
 
 To support this functionality,
@@ -747,11 +748,11 @@ which is the average inflammation per patient across all days.
 > it matter if the change in inflammation is an increase or a decrease?
 >
 > > ## Solution
-> > By using the `numpy.max()` function after you apply the `numpy.diff()`
+> > By using the `numpy.amax()` function after you apply the `numpy.diff()`
 > > function, you will get the largest difference between days.
 > >
 > > ~~~
-> > numpy.max(numpy.diff(data, axis=1), axis=1)
+> > numpy.amax(numpy.diff(data, axis=1), axis=1)
 > > ~~~
 > > {: .language-python}
 > >
@@ -774,7 +775,7 @@ which is the average inflammation per patient across all days.
 > > between readings.
 > >
 > > ~~~
-> > numpy.max(numpy.absolute(numpy.diff(data, axis=1)), axis=1)
+> > numpy.amax(numpy.absolute(numpy.diff(data, axis=1)), axis=1)
 > > ~~~
 > > {: .language-python}
 > >

_episodes/03-matplotlib.md

@@ -74,15 +74,15 @@ the medication takes 3 weeks to take effect.  But a good data scientist doesn't
 average of a dataset, so let's have a look at two other statistics:
 
 ~~~
-max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))
+max_plot = matplotlib.pyplot.plot(numpy.amax(data, axis=0))
 matplotlib.pyplot.show()
 ~~~
 {: .language-python}
 
 ![A line graph showing the maximum inflammation across all patients over a 40-day period.](../fig/inflammation-01-maximum.svg)
 
 ~~~
-min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))
+min_plot = matplotlib.pyplot.plot(numpy.amin(data, axis=0))
 matplotlib.pyplot.show()
 ~~~
 {: .language-python}
@@ -123,10 +123,10 @@ axes1.set_ylabel('average')
 axes1.plot(numpy.mean(data, axis=0))
 
 axes2.set_ylabel('max')
-axes2.plot(numpy.max(data, axis=0))
+axes2.plot(numpy.amax(data, axis=0))
 
 axes3.set_ylabel('min')
-axes3.plot(numpy.min(data, axis=0))
+axes3.plot(numpy.amin(data, axis=0))
 
 fig.tight_layout()
 
@@ -199,7 +199,7 @@ formats, including SVG, PDF, and JPEG.
 > > ~~~
 > > # One method
 > > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
+> > axes3.plot(numpy.amin(data, axis=0))
 > > axes3.set_ylim(0,6)
 > > ~~~
 > > {: .language-python}
@@ -208,10 +208,10 @@ formats, including SVG, PDF, and JPEG.
 > > ## Solution
 > > ~~~
 > > # A more automated approach
-> > min_data = numpy.min(data, axis=0)
+> > min_data = numpy.amin(data, axis=0)
 > > axes3.set_ylabel('min')
 > > axes3.plot(min_data)
-> > axes3.set_ylim(numpy.min(min_data), numpy.max(min_data) * 1.1)
+> > axes3.set_ylim(numpy.amin(min_data), numpy.amax(min_data) * 1.1)
 > > ~~~
 > > {: .language-python}
 > {: .solution}
@@ -244,10 +244,10 @@ formats, including SVG, PDF, and JPEG.
 > > axes1.plot(numpy.mean(data, axis=0), drawstyle='steps-mid')
 > >
 > > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0), drawstyle='steps-mid')
+> > axes2.plot(numpy.amax(data, axis=0), drawstyle='steps-mid')
 > >
 > > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0), drawstyle='steps-mid')
+> > axes3.plot(numpy.amin(data, axis=0), drawstyle='steps-mid')
 > >
 > > fig.tight_layout()
 > >
@@ -297,10 +297,10 @@ formats, including SVG, PDF, and JPEG.
 > > axes1.plot(numpy.mean(data, axis=0))
 > >
 > > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0))
+> > axes2.plot(numpy.amax(data, axis=0))
 > >
 > > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
+> > axes3.plot(numpy.amin(data, axis=0))
 > >
 > > fig.tight_layout()
 > >

_episodes/06-files.md

@@ -71,10 +71,10 @@ for filename in filenames:
     axes1.plot(numpy.mean(data, axis=0))
 
     axes2.set_ylabel('max')
-    axes2.plot(numpy.max(data, axis=0))
+    axes2.plot(numpy.amax(data, axis=0))
 
     axes3.set_ylabel('min')
-    axes3.plot(numpy.min(data, axis=0))
+    axes3.plot(numpy.amin(data, axis=0))
 
     fig.tight_layout()
     matplotlib.pyplot.show()
@@ -200,10 +200,10 @@ flare-ups at all throughout the trial, suggesting that they may not even suffer
 > > axes1.plot(numpy.mean(composite_data, axis=0))
 > >
 > > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(composite_data, axis=0))
+> > axes2.plot(numpy.amax(composite_data, axis=0))
 > >
 > > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(composite_data, axis=0))
+> > axes3.plot(numpy.amin(composite_data, axis=0))
 > >
 > > fig.tight_layout()
 > >

_episodes/07-cond.md

@@ -168,8 +168,8 @@ if maximum inflammation in the beginning (day 0) and in the middle (day 20) of
 the study are equal to the corresponding day numbers.
 
 ~~~
-max_inflammation_0 = numpy.max(data, axis=0)[0]
-max_inflammation_20 = numpy.max(data, axis=0)[20]
+max_inflammation_0 = numpy.amax(data, axis=0)[0]
+max_inflammation_20 = numpy.amax(data, axis=0)[20]
 
 if max_inflammation_0 == 0 and max_inflammation_20 == 20:
     print('Suspicious looking maxima!')
@@ -181,7 +181,7 @@ the minima per day were all zero (looks like a healthy person snuck into our stu
 We can also check for this with an `elif` condition:
 
 ~~~
-elif numpy.sum(numpy.min(data, axis=0)) == 0:
+elif numpy.sum(numpy.amin(data, axis=0)) == 0:
     print('Minima add up to zero!')
 ~~~
 {: .language-python}
@@ -199,12 +199,12 @@ Let's test that out:
 ~~~
 data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
 
-max_inflammation_0 = numpy.max(data, axis=0)[0]
-max_inflammation_20 = numpy.max(data, axis=0)[20]
+max_inflammation_0 = numpy.amax(data, axis=0)[0]
+max_inflammation_20 = numpy.amax(data, axis=0)[20]
 
 if max_inflammation_0 == 0 and max_inflammation_20 == 20:
     print('Suspicious looking maxima!')
-elif numpy.sum(numpy.min(data, axis=0)) == 0:
+elif numpy.sum(numpy.amin(data, axis=0)) == 0:
     print('Minima add up to zero!')
 else:
     print('Seems OK!')
@@ -219,12 +219,12 @@ Suspicious looking maxima!
 ~~~
 data = numpy.loadtxt(fname='inflammation-03.csv', delimiter=',')
 
-max_inflammation_0 = numpy.max(data, axis=0)[0]
-max_inflammation_20 = numpy.max(data, axis=0)[20]
+max_inflammation_0 = numpy.amax(data, axis=0)[0]
+max_inflammation_20 = numpy.amax(data, axis=0)[20]
 
 if max_inflammation_0 == 0 and max_inflammation_20 == 20:
     print('Suspicious looking maxima!')
-elif numpy.sum(numpy.min(data, axis=0)) == 0:
+elif numpy.sum(numpy.amin(data, axis=0)) == 0:
     print('Minima add up to zero!')
 else:
     print('Seems OK!')

_episodes/08-func.md

@@ -241,10 +241,10 @@ def visualize(filename):
     axes1.plot(numpy.mean(data, axis=0))
 
     axes2.set_ylabel('max')
-    axes2.plot(numpy.max(data, axis=0))
+    axes2.plot(numpy.amax(data, axis=0))
 
     axes3.set_ylabel('min')
-    axes3.plot(numpy.min(data, axis=0))
+    axes3.plot(numpy.amin(data, axis=0))
 
     fig.tight_layout()
     matplotlib.pyplot.show()
@@ -259,9 +259,9 @@ def detect_problems(filename):
 
     data = numpy.loadtxt(fname=filename, delimiter=',')
 
-    if numpy.max(data, axis=0)[0] == 0 and numpy.max(data, axis=0)[20] == 20:
+    if numpy.amax(data, axis=0)[0] == 0 and numpy.amax(data, axis=0)[20] == 20:
         print('Suspicious looking maxima!')
-    elif numpy.sum(numpy.min(data, axis=0)) == 0:
+    elif numpy.sum(numpy.amin(data, axis=0)) == 0:
         print('Minima add up to zero!')
     else:
         print('Seems OK!')
@@ -349,12 +349,12 @@ It's hard to tell from the default output whether the result is correct,
 but there are a few tests that we can run to reassure us:
 
 ~~~
-print('original min, mean, and max are:', numpy.min(data), numpy.mean(data), numpy.max(data))
+print('original min, mean, and max are:', numpy.amin(data), numpy.mean(data), numpy.amax(data))
 offset_data = offset_mean(data, 0)
 print('min, mean, and max of offset data are:',
-      numpy.min(offset_data),
+      numpy.amin(offset_data),
       numpy.mean(offset_data),
-      numpy.max(offset_data))
+      numpy.amax(offset_data))
 ~~~
 {: .language-python}
 
@@ -825,8 +825,8 @@ readable code!
 > > ## Solution
 > > ~~~
 > > def rescale(input_array):
-> >     L = numpy.min(input_array)
-> >     H = numpy.max(input_array)
+> >     L = numpy.amin(input_array)
+> >     H = numpy.amax(input_array)
 > >     output_array = (input_array - L) / (H - L)
 > >     return output_array
 > > ~~~
@@ -870,8 +870,8 @@ readable code!
 > > ~~~
 > > def rescale(input_array, low_val=0.0, high_val=1.0):
 > >     """rescales input array values to lie between low_val and high_val"""
-> >     L = numpy.min(input_array)
-> >     H = numpy.max(input_array)
+> >     L = numpy.amin(input_array)
+> >     H = numpy.amax(input_array)
 > >     intermed_array = (input_array - L) / (H - L)
 > >     output_array = intermed_array * (high_val - low_val) + low_val
 > >     return output_array

_episodes/10-defensive.md

@@ -489,7 +489,7 @@ This violates another important rule of programming:
 > > # a possible pre-condition:
 > > assert len(input_list) > 0, 'List length must be non-zero'
 > > # a possible post-condition:
-> > assert numpy.min(input_list) <= average <= numpy.max(input_list),
+> > assert numpy.amin(input_list) <= average <= numpy.amax(input_list),
 > > 'Average should be between min and max of input values (inclusive)'
 > > ~~~
 > > {: .language-python}

_episodes/12-cmdline.md

@@ -460,11 +460,11 @@ def main():
         data = numpy.loadtxt(filename, delimiter=',')
 
         if action == '--min':
-            values = numpy.min(data, axis=1)
+            values = numpy.amin(data, axis=1)
         elif action == '--mean':
             values = numpy.mean(data, axis=1)
         elif action == '--max':
-            values = numpy.max(data, axis=1)
+            values = numpy.amax(data, axis=1)
 
         for val in values:
             print(val)
@@ -527,11 +527,11 @@ def process(filename, action):
     data = numpy.loadtxt(filename, delimiter=',')
 
     if action == '--min':
-        values = numpy.min(data, axis=1)
+        values = numpy.amin(data, axis=1)
     elif action == '--mean':
         values = numpy.mean(data, axis=1)
     elif action == '--max':
-        values = numpy.max(data, axis=1)
+        values = numpy.amax(data, axis=1)
 
     for val in values:
         print(val)
@@ -632,11 +632,11 @@ def process(filename, action):
     data = numpy.loadtxt(filename, delimiter=',')
 
     if action == '--min':
-        values = numpy.min(data, axis=1)
+        values = numpy.amin(data, axis=1)
     elif action == '--mean':
         values = numpy.mean(data, axis=1)
     elif action == '--max':
-        values = numpy.max(data, axis=1)
+        values = numpy.amax(data, axis=1)
 
     for val in values:
         print(val)
@@ -793,11 +793,11 @@ the program now does everything we set out to do.
 > >     data = numpy.loadtxt(filename, delimiter=',')
 > >
 > >     if action == '-n':
-> >         values = numpy.min(data, axis=1)
+> >         values = numpy.amin(data, axis=1)
 > >     elif action == '-m':
 > >         values = numpy.mean(data, axis=1)
 > >     elif action == '-x':
-> >         values = numpy.max(data, axis=1)
+> >         values = numpy.amax(data, axis=1)
 > >
 > >     for val in values:
 > >         print(val)
@@ -844,11 +844,11 @@ the program now does everything we set out to do.
 > >     data = numpy.loadtxt(filename, delimiter=',')
 > >
 > >     if action == '--min':
-> >         values = numpy.min(data, axis=1)
+> >         values = numpy.amin(data, axis=1)
 > >     elif action == '--mean':
 > >         values = numpy.mean(data, axis=1)
 > >     elif action == '--max':
-> >         values = numpy.max(data, axis=1)
+> >         values = numpy.amax(data, axis=1)
 > >
 > >     for val in values:
 > >         print(val)
@@ -890,11 +890,11 @@ the program now does everything we set out to do.
 > >     data = numpy.loadtxt(filename, delimiter=',')
 > >
 > >     if action == '--min':
-> >         values = numpy.min(data, axis=1)
+> >         values = numpy.amin(data, axis=1)
 > >     elif action == '--mean':
 > >         values = numpy.mean(data, axis=1)
 > >     elif action == '--max':
-> >         values = numpy.max(data, axis=1)
+> >         values = numpy.amax(data, axis=1)
 > >
 > >     for val in values:
 > >         print(val)