find maxima for plateaus

hands_on/local_maxima/local_maxima.py

@@ -1,22 +1,37 @@
-def local_maxima(x):
-    """Find local maxima of x.
+import numpy as np
+from typing import Union
+from numpy.typing import NDArray
+import sys
 
+def find_maxima(numbers: Union[list, NDArray]):
+    """Find local maxima of x.
     Example:
     >>> x = [1, 3, -2, 0, 2, 1]
     >>> find_maxima(x)
     [1, 4]
-
     If in a local maximum several elements have the same value,
     return the left-most index.
     Example:
     >>> x = [1, 2, 2, 1]
     >>> find_maxima(x)
     [1]
-
     Input arguments:
     x -- 1D list of real numbers
-
     Output:
     idx -- list of indices of the local maxima in x
     """
-    return []
+    max_idxs = []
+    for ni, num in enumerate(numbers[:-1]):
+        if ni == 0:
+            pass
+        elif (numbers[ni+1] <= num and numbers[ni-1] < num):
+            max_idxs.append(ni)
+
+    return max_idxs
+
+if __name__ == '__main__':
+    numbers = np.array(sys.argv[1:], dtype=np.int_)
+    max_idxs = find_maxima(numbers)
+    print(max_idxs)
+
+

hands_on/local_maxima/test_local_maxima.py

@@ -1,27 +1,30 @@
-from local_maxima import local_maxima
+from local_maxima import find_maxima
 
 
-def test_local_maxima():
+def test_find_maxima():
     values = [1, 3, -2, 0, 2, 1]
     expected = [1, 4]
-    maxima = local_maxima(values)
+    maxima = find_maxima(values)
     assert maxima == expected
 
 
-def test_local_maxima_negative():
+def test_find_maxima_negative():
     values = [-1, -1, 0, -1]
     expected = [2]
-    maxima = local_maxima(values)
+    maxima = find_maxima(values)
     assert maxima == expected
 
 
-def test_local_maxima_edges():
+def test_find_maxima_edges():
     values = [4, 2, 1, 3, 1, 5]
-    expected = [0, 3, 5]
-    maxima = local_maxima(values)
+    expected = [3]
+    maxima = find_maxima(values)
     assert maxima == expected
 
 
-def test_local_maxima_plateau():
-    raise Exception('not yet implemented')
+def test_find_maxima_plateau():
+    values = [1,2,2,3,4,4,2]
+    expected = [1,4]
+    maxima = find_maxima(values)
+    assert maxima == expected