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NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
QuTiP, short for the Quantum Toolbox in Python, is an open-source computational physics software library for simulating quantum systems, particularly open quantum systems. [1] [2] QuTiP allows simulation of Hamiltonians with arbitrary time-dependence, allowing simulation of situations of interest in quantum optics, ion trapping, superconducting circuits and quantum nanomechanical resonators.
NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
A Dask array comprises many smaller n-dimensional Numpy arrays and uses a blocked algorithm to enable computation on larger-than-memory arrays. During an operation, Dask translates the array operation into a task graph, breaks up large Numpy arrays into multiple smaller chunks, and executes the work on each chunk in parallel.
Special array types are often defined by the language's standard libraries. Dynamic lists are also more common and easier to implement [dubious – discuss] than dynamic arrays. Array types are distinguished from record types mainly because they allow the element indices to be computed at run time, as in the Pascal assignment A[I,J] := A[N-I,2*J].
Both libraries were merged into a new array package by Travis Oliphant in 2005–2006, creating NumPy, now the de facto standard for numerical data handling in Python. [10] In the following years the existing software packages maintained by STScI as part of their stsci_python suite were ported to NumPy as well.
In addition to support for vectorized arithmetic and relational operations, these languages also vectorize common mathematical functions such as sine. For example, if x is an array, then y = sin (x) will result in an array y whose elements are sine of the corresponding elements of the array x. Vectorized index operations are also supported.