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Welcome to PyDataStructs's documentation!
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=========================================
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- This project aims to be a Python package for various data
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- structures in computer science. We are also working on the
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- development of algorithms including their parallel implementations.
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- To the best of our knowledge, a well-designed library/package which
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+ This project aims to be a Python package for various data
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+ structures in computer science. We are also working on the
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+ development of algorithms including their parallel implementations.
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+ To the best of our knowledge, a well-designed library/package which
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has covered most of the data structures and algorithms doesn't exist yet.
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- Once the software design becomes more stable after a few releases of
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- this package in the near future, we also aim to provide APIs for the
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+ Once the software design becomes more stable after a few releases of
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+ this package in the near future, we also aim to provide APIs for the
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code in C++ and Java as well.
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.. note ::
@@ -23,14 +23,14 @@ code in C++ and Java as well.
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Installation
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============
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- After changing your directory to project root, you can
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+ After changing your directory to project root, you can
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install the package by running the following command,
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- ``python -m pip install . ``
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+ ``python scripts/build/ install.py ``
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- For development purposes, you can use the option ` e ` as shown below ,
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+ For development purposes, i.e., if you intend to be a contributor ,
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- ``python -m pip install -e . ``
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+ ``python scripts/build/develop.py ``
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For building documentation execute the following commands one after
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the other,
@@ -58,13 +58,13 @@ So, **you can easily rely on PyDataStructs** for any data structure or algorithm
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Why do we use Python?
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=====================
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- As we know Python is an interpreted language and hence is
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- slow compared to C++, the most popular language for competitive programming.
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- We still decided to use Python because the software development can happen
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- at a much faster pace and it is much easier to test various software designs
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- and APIs as coding them out takes no time. However, keeping the need of the
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- users in mind, we will shift to C++ backend, which will happen quickly as
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- we would be required to just translate the tested code rather than writing it
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+ As we know Python is an interpreted language and hence is
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+ slow compared to C++, the most popular language for competitive programming.
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+ We still decided to use Python because the software development can happen
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+ at a much faster pace and it is much easier to test various software designs
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+ and APIs as coding them out takes no time. However, keeping the need of the
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+ users in mind, we will shift to C++ backend, which will happen quickly as
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+ we would be required to just translate the tested code rather than writing it
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from scratch, after a few releases with APIs available for all the languages.
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Contents
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