fix: use amin/amax from numpy

Signed-off-by: Henry Schreiner <[email protected]>

Author: henryiii

_episodes/02-numpy.md

@@ -18,7 +18,7 @@ keypoints:
 - "Array indices start at 0, not 1."
 - "Use `low:high` to specify a `slice` that includes the indices from `low` to `high-1`."
 - "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)`, `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."
 ---
 
@@ -359,16 +359,16 @@ 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
@@ -402,7 +402,7 @@ 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}
 
@@ -420,7 +420,7 @@ 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}
 
@@ -435,7 +435,7 @@ 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,
@@ -754,11 +754,11 @@ 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}
 > >
@@ -781,7 +781,7 @@ 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

@@ -222,10 +222,10 @@ 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()
@@ -240,9 +240,9 @@ 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!')
@@ -330,12 +330,12 @@ 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}
 
@@ -806,8 +806,8 @@ 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
 > > ~~~
@@ -851,8 +851,8 @@ 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

@@ -482,7 +482,7 @@ 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)