49 lines
2.0 KiB
ReStructuredText
49 lines
2.0 KiB
ReStructuredText
==============
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Scope of NumPy
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==============
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Here, we describe aspects of N-d array computation that are within scope for NumPy development. This is *not* an aspirational definition of where NumPy should aim, but instead captures the status quo—areas which we have decided to continue supporting, at least for the time being.
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- **In-memory, N-dimensional, homogeneously typed (single pointer + strided) arrays on CPUs**
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- Support for a wide range of data types
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- Not specialized hardware such as GPUs
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- But, do support wide range of CPUs (e.g. ARM, PowerX)
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- **Higher level APIs for N-dimensional arrays**
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- NumPy is a *de facto* standard for array APIs in Python
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- Indexing and fast iteration over elements (ufunc)
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- Interoperability protocols with other data container implementations (like
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:ref:`__array_ufunc__ and __array_function__ <basics.dispatch>`.
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- **Python API and a C API** to the ndarray's methods and attributes.
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- Other **specialized types or uses of N-dimensional arrays**:
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- Masked arrays
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- Structured arrays (informally known as record arrays)
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- Memory mapped arrays
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- Historically, NumPy has included the following **basic functionality
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in support of scientific computation**. We intend to keep supporting
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(but not to expand) what is currently included:
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- Linear algebra
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- Fast Fourier transforms and windowing
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- Pseudo-random number generators
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- Polynomial fitting
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- NumPy provides some **infrastructure for other packages in the scientific Python ecosystem**:
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- numpy.distutils (build support for C++, Fortran, BLAS/LAPACK, and other
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relevant libraries for scientific computing)
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- f2py (generating bindings for Fortran code)
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- testing utilities
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- **Speed**: we take performance concerns seriously and aim to execute
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operations on large arrays with similar performance as native C
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code. That said, where conflict arises, maintenance and portability take
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precedence over performance. We aim to prevent regressions where
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possible (e.g., through asv).
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