heaps.py supports list, tuple and pydatastructs.Array

pydatastructs/trees/heaps.py

@@ -1,6 +1,6 @@
 from pydatastructs.utils.misc_util import _check_type, NoneType, TreeNode, BinomialTreeNode
 from pydatastructs.linear_data_structures.arrays import (ArrayForTrees,
-     DynamicOneDimensionalArray)
+     DynamicOneDimensionalArray, Array)
 from pydatastructs.miscellaneous_data_structures.binomial_trees import BinomialTree
 
 __all__ = [
@@ -24,7 +24,7 @@ class DHeap(Heap):
     Parameters
     ==========
 
-    elements : list, tuple
+    elements : list, tuple, Array
         Optional, by default 'None'.
         List/tuple of initial TreeNode in Heap.
 
@@ -84,9 +84,15 @@ def __new__(cls, elements=None, heap_property="min", d=4):
             raise ValueError("%s is invalid heap property"%(heap_property))
         if elements is None:
             elements = DynamicOneDimensionalArray(TreeNode, 0)
-        else:
+        elif type(elements) in (list,tuple):
             if not all(map(lambda x: _check_type(x, TreeNode), elements)):
-                raise ValueError("Expect a list/tuple of TreeNode got %s"%(elements))
+                raise ValueError("Expected a list/tuple/Array of TreeNode got %s"%(elements))
+            elements = DynamicOneDimensionalArray(TreeNode, len(elements), elements)
+        elif _check_type(elements, Array):
+            if(not _check_type(elements[0], TreeNode)):
+                raise ValueError(f'Expected a list/tuple/Array of TreeNode got {type(elements[0])}')
+        else:
+            raise ValueError(f'Expected a list/tuple/Array of TreeNode got {type(elements)}')
         obj.heap = elements
         obj._last_pos_filled = obj.heap._last_pos_filled
         obj._build()
@@ -326,7 +332,7 @@ class BinomialHeap(Heap):
     Parameters
     ==========
 
-    root_list: list/tuple
+    root_list: list/tuple/Array
         By default, []
         The list of BinomialTree object references
         in sorted order.

pydatastructs/trees/tests/test_heaps.py

@@ -41,7 +41,7 @@ def test_BinaryHeap():
                 TreeNode(1, 1), TreeNode(2, 2), TreeNode(3, 3),
                 TreeNode(17, 17), TreeNode(19, 19), TreeNode(36, 36)
             ]
-    min_heap = BinaryHeap(elements=DynamicOneDimensionalArray(TreeNode, 9, elements), heap_property="min")
+    min_heap = BinaryHeap(elements=elements, heap_property="min")
     assert min_heap.extract().key == 1
 
     expected_sorted_elements = [2, 3, 7, 17, 19, 25, 36, 100]
@@ -55,6 +55,17 @@ def test_BinaryHeap():
             ]
     assert raises(ValueError, lambda:
                 BinaryHeap(elements = non_TreeNode_elements, heap_property='min'))
+
+    non_TreeNode_elements = DynamicOneDimensionalArray(int, 0)
+    non_TreeNode_elements.append(1)
+    non_TreeNode_elements.append(2)
+    assert raises(ValueError, lambda:
+                BinaryHeap(elements = non_TreeNode_elements, heap_property='min'))
+
+    non_heapable = "[1, 2, 3]"
+    assert raises(ValueError, lambda:
+                BinaryHeap(elements = non_heapable, heap_property='min'))
+
 def test_TernaryHeap():
     max_heap = TernaryHeap(heap_property="max")
     assert raises(IndexError, lambda: max_heap.extract())
@@ -86,7 +97,7 @@ def test_TernaryHeap():
         TreeNode(1, 1), TreeNode(2, 2), TreeNode(3, 3),
         TreeNode(17, 17), TreeNode(19, 19), TreeNode(36, 36)
     ]
-    min_heap = TernaryHeap(elements=DynamicOneDimensionalArray(TreeNode, 9, elements), heap_property="min")
+    min_heap = TernaryHeap(elements=elements, heap_property="min")
     expected_extracted_element = min_heap.heap[0].key
     assert min_heap.extract().key == expected_extracted_element