Python functions for working with Dary Heap (Heap with more than 2 child nodes)
Project description
Python functions for working with Dary Heap (Heap with more than 2 child nodes). For more info about this Data Structure Please gothrough: https://en.wikipedia.org/wiki/Dary_heap
This library provides the below Heap specific functions.
 heapify
 Convert list of elements to Heap data structure (MinHeap/MaxHeap)
 add_element
 Add single/list of elements to Heap
 get_root_value
 Returns root value of the Heap without removing the element Minimum value for Min Heap, Maximum value for Max Heap
 extract_root
 Extract root element from Heap and reform the Heap
 search_value
 Searches the value in heap and returns index. if same element is present multiple times, first occurring index is returned
 delete_element_at_index
 Remove the element at the specified index and reform the Heap
For example function invocations, plesae see the tutorial.
Installation
install from pypi using pip:
$ pip install d_heap
or install from source using:
$ git clone https://github.com/rameshrvr/dary_heap.git $ cd dary_heap $ pip install .
Tutorial
 Min Heap (Heap where the data in parent node is lesser than the data in child node)
RameshsMacBookPro:dary_heap rameshrv$ python3 Python 3.7.2 (default, Dec 27 2018, 07:35:06) [Clang 10.0.0 (clang1000.11.45.5)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> >>> from d_heap import MinHeap, MaxHeap >>> >>> array = [4, 3, 6, 8, 11, 1, 5, 14, 10, 7, 2, 12, 9, 13, 15] >>> >>> min_heap_4_children = MinHeap(4, array) # Convert array to 4 children Heap >>> >>> min_heap_4_children.elements() [1, 3, 2, 8, 11, 4, 5, 14, 10, 7, 6, 12, 9, 13, 15] >>> >>> min_heap_5_children = MinHeap(5, array) # Convert array to 5 children Heap >>> >>> min_heap_5_children.elements() [1, 2, 6, 8, 11, 4, 5, 14, 10, 7, 3, 12, 9, 13, 15] >>> >>> min_heap_4_children.add_element(0) # Add single element to Heap >>> >>> min_heap_4_children.elements() [0, 3, 2, 1, 11, 4, 5, 14, 10, 7, 6, 12, 9, 13, 15, 8] >>> >>> min_heap_5_children.add_element([0, 24, 17, 55]) # Add list of elements to heap >>> >>> min_heap_5_children.elements() [0, 2, 1, 8, 11, 4, 5, 14, 10, 7, 3, 12, 9, 13, 15, 6, 24, 17, 55] >>> >>> min_heap_4_children.extract_root() # Extract root element from Heap and retrun it. In this case its the minimum element 0 >>> >>> min_heap_4_children.elements() [1, 3, 2, 8, 11, 4, 5, 14, 10, 7, 6, 12, 9, 13, 15] >>> >>> min_heap_4_children.get_root_value() # Returns the root value (minimum value) without removing it from Heap 1 >>> >>> min_heap_4_children.search_value(5) # Returns index of the searched value. 1 if there is no such value in Heap 6 >>> min_heap_4_children.search_value(7) 9 >>> min_heap_4_children.search_value(21) 1 >>> >>> min_heap_4_children.delete_element_at_index(4) # Remove the element at the specified index >>> >>> min_heap_4_children.elements() [1, 3, 2, 8, 15, 4, 5, 14, 10, 7, 6, 12, 9, 13] >>>
 Max Heap (Heap where the data in parent node is greater than the data in child node)
RameshsMacBookPro:dary_heap rameshrv$ python3 Python 3.7.2 (default, Dec 27 2018, 07:35:06) [Clang 10.0.0 (clang1000.11.45.5)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> >>> from d_heap import MinHeap, MaxHeap >>> >>> array = [4, 3, 6, 8, 11, 1, 5, 14, 10, 7, 2, 12, 9, 13, 15] >>> >>> max_heap_4_children = MaxHeap(4, array) # Convert array to 4 children Heap >>> >>> max_heap_4_children.elements() [15, 14, 12, 13, 11, 1, 5, 3, 10, 7, 2, 6, 9, 4, 8] >>> >>> max_heap_5_children = MaxHeap(5, array) # Convert array to 5 children Heap >>> >>> max_heap_5_children.elements() [15, 14, 13, 8, 11, 1, 5, 3, 10, 7, 2, 12, 9, 4, 6] >>> >>> max_heap_4_children.add_element(21) # Add single element to Heap >>> >>> max_heap_4_children.elements() [21, 14, 12, 15, 11, 1, 5, 3, 10, 7, 2, 6, 9, 4, 8, 13] >>> >>> >>> max_heap_5_children.add_element([21, 14, 27, 35]) # Add list of elements to heap >>> >>> max_heap_5_children.elements() [35, 14, 15, 27, 11, 1, 5, 3, 10, 7, 2, 12, 9, 4, 6, 13, 8, 14, 21] >>> >>> max_heap_4_children.extract_root() # Extract root element from Heap and retrun it. In this case its the maximum element 21 >>> >>> max_heap_4_children.elements() [15, 14, 12, 13, 11, 1, 5, 3, 10, 7, 2, 6, 9, 4, 8] >>> >>> max_heap_4_children.get_root_value() # Returns the root value (maximum value) without removing it from Heap 15 >>> >>> max_heap_4_children.search_value(5) # Returns index of the searched value. 1 if there is no such value in Heap 6 >>> max_heap_4_children.search_value(11) 4 >>> max_heap_4_children.search_value(21) 1 >>> >>> max_heap_4_children.delete_element_at_index(2) # Remove the element at the specified index >>> >>> max_heap_4_children.elements() [15, 14, 9, 13, 11, 1, 5, 3, 10, 7, 2, 6, 8, 4] >>>
Development
After checking out the repo, cd to the repository. Then, run pip install . to install the package locally. You can also run python (or) python3 for an interactive prompt that will allow you to experiment.
To install this package onto your local machine, cd to the repository then run pip install .. To release a new version, update the version number in setup.py, and then run python setup.py register, which will create a git tag for the version, push git commits and tags, and push the package file to [PyPI](https://pypi.org).
Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/rameshrvr/dary_heap. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant]<http://contributorcovenant.org> code of conduct.
License
The package is available as open source under the terms of the [MIT License]<https://opensource.org/licenses/MIT>.
Code of Conduct
Everyone interacting in the Binary Heap project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the [code of conduct](https://github.com/rameshrvr/dary_heap/blob/master/CODE_OF_CONDUCT.md).
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