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CMake VS NumPy

Compare CMake VS NumPy and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

CMake logo CMake

CMake is an open-source, cross-platform family of tools designed to build, test and package software.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • CMake Landing page
    Landing page //
    2022-09-21

We recommend LibHunt CMake for discovery and comparisons of trending CMake projects.

  • NumPy Landing page
    Landing page //
    2023-05-13

CMake features and specs

  • Cross-platform support
    CMake is designed to support multiple operating systems including Windows, macOS, and Linux. This allows developers to write platform-independent CMake scripts.
  • Build tool agnostic
    CMake can generate build files for a variety of build systems including Makefiles, Ninja, and Visual Studio solutions. This means developers are not tied to a specific build tool.
  • Large community and extensive documentation
    CMake has a large user base and an extensive amount of documentation and tutorials available which can be helpful for new and experienced users alike.
  • Integrated testing support
    CMake includes support for testing frameworks such as CTest, which allows for automated testing of code during the build process.
  • Modular and scalable
    CMake is highly modular, enabling users to create reusable and maintainable code by organizing CMake scripts into libraries and modules.

Possible disadvantages of CMake

  • Steep learning curve
    CMake's complexity and its extensive range of features can be difficult for beginners to grasp, leading to a steep learning curve.
  • Verbose syntax
    CMake scripts can often become verbose and difficult to read, especially for large projects. This can make maintenance and debugging challenging.
  • Inconsistent module quality
    The quality and support of different CMake modules can vary, sometimes leading to issues with compatibility or functionality.
  • Performance overhead
    CMake may introduce some performance overhead during the configuration process, especially for very large projects.
  • Complexity in advanced features
    Some of the more advanced features of CMake, such as custom commands and complex dependency management, can be quite difficult to implement correctly.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of CMake

Overall verdict

  • CMake is generally considered a good tool for managing the build process of software projects, especially those with a complex codebase that spans multiple platforms.

Why this product is good

  • Flexibility
    It offers great flexibility in terms of defining build processes, enabling advanced configuration and optimization techniques to be used.
  • Integration
    It integrates well with many popular IDEs and other tools, providing a smoother development experience.
  • Wide adoption
    CMake is widely used in the industry, which leads to robust community support and regular updates.
  • Cross platform support
    CMake is designed to support multiple platforms, which makes it highly valuable for projects that need to be compiled and run on different operating systems.

Recommended for

  • projects requiring cross-platform compatibility
  • developers looking for a powerful build configuration tool
  • complex software projects with numerous dependencies
  • teams that value strong community and industry support

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

CMake videos

+ Add

CMake for Dummies

More videos:

  • Review - CppCon 2017: Mathieu Ropert โ€œUsing Modern CMake Patterns to Enforce a Good Modular Designโ€
  • Review - Hunter, a CMake driven package manager for C/C++ projects - Daniel Friedrich - Lightning Talks

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to CMake and NumPy)

CMake

NumPy

Front End Package Manager
100 100%
Front End Package Manager
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
JS Build Tools
100 100%
JS Build Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare CMake and NumPy

CMake Reviews

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NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy should be more popular than CMake. It has been mentiond 122 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

CMake mentions (55)

  • How I deployed my first project for my devops portfolio: Project Architecture
    I used CMAKE as my compiling tool followed by make. - Source: dev.to / 12 months ago
  • DeadLock: Research Results & Tech Stack
    All this C++ project can't be ran as simple C++ code, so I will be building this whole package using CMake. It will streamline building this project onto other computers. - Source: dev.to / about 1 year ago
  • Master This Feature of DevEco Studio to Efficiently Implement ArkTS and C++ Glue Code
    For knowledge in this aspect, you can refer to the relevant documents of the CMake build tool: https://cmake.org/. - Source: dev.to / over 1 year ago
  • Creating a Native Desktop GUI Using C++ with GTK
    I used CMAKE to define the build configurations. I find it very convenient that CMAKE generates the Makefile on Linux and can also create a Visual Studio project on Windows. - Source: dev.to / over 1 year ago
  • Top 7 C++ Tools to explore in 2024 if it's not already the case.
    CMake stands for "Cross-platform Make" and is an open-source, platform-independent build system. It's designed to build, test, and package software projects written in C and C++, but it can also be used for other languages. Here's an overview of CMake and its features:. - Source: dev.to / over 2 years ago
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NumPy mentions (122)

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What are some alternatives?

When comparing CMake and NumPy, you can also consider the following products

GNU Make - GNU Make is a tool which controls the generation of executables and other non-source files of a program from the program's source files.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

SCons - SCons is an Open Source software construction toolโ€”that is, a next-generation build tool.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

SBT - SBT is a build tool for Scala, like Ant or Maven but with hieroglyphics.

OpenCV - OpenCV is the world's biggest computer vision library

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