graph LR
Core_Array_API["Core Array API"]
Masked_Array_Module["Masked Array Module"]
Linear_Algebra_Module["Linear Algebra Module"]
FFT_Module["FFT Module"]
Random_Number_Generation["Random Number Generation"]
Polynomial_Module["Polynomial Module"]
I_O_and_Data_Persistence["I-O and Data Persistence"]
Array_Utilities_and_Type_System["Array Utilities and Type System"]
Build_System_and_F2PY["Build System and F2PY"]
Testing_Framework["Testing Framework"]
Core_Array_API -- "provides core functionality for" --> Masked_Array_Module
Core_Array_API -- "provides core functionality for" --> Linear_Algebra_Module
Core_Array_API -- "provides core functionality for" --> FFT_Module
Core_Array_API -- "provides core functionality for" --> Random_Number_Generation
Core_Array_API -- "provides core functionality for" --> Polynomial_Module
Core_Array_API -- "provides core functionality for" --> I_O_and_Data_Persistence
Core_Array_API -- "provides core functionality for" --> Array_Utilities_and_Type_System
Masked_Array_Module -- "uses" --> Core_Array_API
Linear_Algebra_Module -- "relies on" --> Build_System_and_F2PY
Polynomial_Module -- "uses" --> Linear_Algebra_Module
Build_System_and_F2PY -- "uses" --> Core_Array_API
Build_System_and_F2PY -- "generates code for" --> Core_Array_API
I_O_and_Data_Persistence -- "supports I/O for" --> Masked_Array_Module
Array_Utilities_and_Type_System -- "provides utilities for" --> Masked_Array_Module
Array_Utilities_and_Type_System -- "uses" --> Linear_Algebra_Module
Testing_Framework -- "tests" --> Core_Array_API
Testing_Framework -- "tests" --> Masked_Array_Module
Testing_Framework -- "tests" --> Linear_Algebra_Module
Testing_Framework -- "tests" --> Random_Number_Generation
Testing_Framework -- "tests" --> Polynomial_Module
Testing_Framework -- "tests" --> I_O_and_Data_Persistence
click Core_Array_API href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Core Array API.md" "Details"
click Masked_Array_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Masked Array Module.md" "Details"
click Linear_Algebra_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Linear Algebra Module.md" "Details"
click FFT_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/FFT Module.md" "Details"
click Random_Number_Generation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Random Number Generation.md" "Details"
click Polynomial_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Polynomial Module.md" "Details"
click I_O_and_Data_Persistence href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/I-O and Data Persistence.md" "Details"
click Array_Utilities_and_Type_System href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Array Utilities and Type System.md" "Details"
click Build_System_and_F2PY href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Build System and F2PY.md" "Details"
click Testing_Framework href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/numpy/Testing Framework.md" "Details"
The NumPy project's architecture is centered around its highly optimized Core Array API, which provides the fundamental data structures and operations for numerical computing. Specialized modules like Masked Array, Linear Algebra, FFT, Random Number Generation, and Polynomials extend this core functionality for specific domains. A robust Build System and F2PY facilitate the integration of high-performance C/Fortran code, while I/O and Data Persistence handle data storage. Array Utilities and Type System provide essential array manipulation tools, and a comprehensive Testing Framework ensures code quality and correctness across all components.
This component provides the fundamental building blocks for NumPy arrays, including array creation, basic element-wise operations, shape manipulation, and the underlying C-level array implementation. It forms the bedrock upon which most other NumPy functionalities are built, offering a high-level interface for common numerical operations.
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This module extends NumPy's array capabilities to handle data with missing or invalid entries using a 'mask'. It provides specialized array types and functions for operations that respect these masks, ensuring calculations only involve valid data. It wraps many core NumPy functions to support masked arrays.
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This component offers a comprehensive set of linear algebra functions, including matrix decompositions (Cholesky, SVD, QR), eigenvalue problems, solving linear equations, and various matrix norms. It relies on optimized underlying libraries for performance and integrates with the Core Array API.
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This module provides a collection of Fast Fourier Transform (FFT) and related functions for signal processing, including 1D, 2D, and N-dimensional FFTs, and helper functions for frequency manipulation. It leverages optimized C implementations for efficiency.
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This component provides tools for generating pseudo-random numbers from various statistical distributions, supporting both legacy and modern BitGenerator-based approaches for reproducibility and statistical quality.
Related Classes/Methods:
numpy.numpy.random.mtrand(full file reference)numpy.numpy.random._generator(full file reference)numpy.numpy.random.bit_generator(full file reference)
This module provides a robust framework for working with various polynomial series, including standard power series. It offers functions for arithmetic, differentiation, integration, root finding, and fitting.
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This component manages reading and writing array data to and from various file formats, including NumPy's native binary formats (.npy, .npz) and common text formats. It also provides utilities for data source management and text parsing.
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This component provides essential utilities for inspecting and manipulating array types, handling non-finite numbers (NaN, Inf), and performing advanced memory-efficient array views through stride manipulation and broadcasting.
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This component provides NumPy's custom build system, extending standard Python distutils to compile C, C++, and Fortran extensions. It includes F2PY, a tool for generating Python interfaces to Fortran code, facilitating the integration of high-performance Fortran routines into NumPy.
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This component provides a comprehensive set of utilities and assertions specifically designed for testing NumPy code. It includes functions for comparing arrays with various tolerances, managing warnings, and tools for performance and memory profiling, crucial for maintaining code quality and correctness.
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