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NumPy VS OpenGrok

Compare NumPy VS OpenGrok and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

OpenGrok logo OpenGrok

OpenGrok is a fast and usable source code search and cross reference engine.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • OpenGrok Landing page
    Landing page //
    2021-10-20

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.

OpenGrok features and specs

  • Efficient Code Search
    OpenGrok provides powerful full-text code search capabilities, which allow developers to quickly find relevant code fragments, classes, and functions across potentially large codebases.
  • Source Code Navigation
    It facilitates easy navigation through source code, enabling users to explore code structure, variable definitions, and references, enhancing understanding and productivity.
  • Supports Multiple Version Control Systems
    OpenGrok is compatible with various version control systems such as Git, Mercurial, and Subversion, making it versatile and adaptable to different development environments.
  • Web Interface
    The tool provides a user-friendly web interface, allowing remote access to code repositories and making it easier for teams to collaborate and share code insights.
  • Cross-Referencing
    OpenGrok includes cross-referencing capabilities that enable developers to identify and analyze code dependencies and connections, improving code comprehension and maintenance.

Possible disadvantages of OpenGrok

  • Initial Setup Complexity
    Setting up OpenGrok can be challenging, requiring considerable configuration and resources, particularly for large and complex codebases.
  • Resource Intensive
    The tool can be resource-intensive, requiring substantial CPU and memory, especially when indexing large repositories, which may impact performance.
  • Limited Language Support
    OpenGrok may not support all programming languages natively for indexing and searching, potentially limiting its applicability in heterogeneous environments.
  • Maintenance Overhead
    Ensuring that OpenGrok remains efficient and up-to-date can entail ongoing maintenance, including regular updates and re-indexing of repositories.
  • Scalability Challenges
    While OpenGrok is powerful, scaling it for very large enterprise environments or numerous users can present challenges, requiring infrastructure considerations and optimizations.

NumPy videos

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

OpenGrok videos

How to setup Opengrok on Linux (In less than 2 minutes)

More videos:

  • Review - Writing and Rewriting Web Apps in nginx.conf — URL shortening, OpenGrok05 by Constantine Murenin

Category Popularity

0-100% (relative to NumPy and OpenGrok)
Data Science And Machine Learning
Git
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Code Collaboration
0 0%
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 NumPy and OpenGrok

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.
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

OpenGrok Reviews

We have no reviews of OpenGrok yet.
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Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 119 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.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 8 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

OpenGrok mentions (0)

We have not tracked any mentions of OpenGrok yet. Tracking of OpenGrok recommendations started around Mar 2021.

What are some alternatives?

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

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

Sourcegraph - Sourcegraph is a free, self-hosted code search and intelligence server that helps developers find, review, understand, and debug code. Use it with any Git code host for teams from 1 to 10,000+.

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

Atlassian Fisheye - With FishEye you can search code, visualize and report on activity and find for commits, files, revisions, or teammates across SVN, Git, Mercurial, CVS and Perforce.

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

Krugle - Krugle is the complete enterprise solution for search targeted to the development organization.