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Atril VS Matplotlib

Compare Atril VS Matplotlib and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Atril logo Atril

Atril is a simple multi-page document viewer. Atril is a fork of Evince.

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • Atril Landing page
    Landing page //
    2021-09-22
  • Matplotlib Landing page
    Landing page //
    2023-06-14

Atril features and specs

  • Lightweight
    Atril is designed to be a lightweight document viewer, making it fast and efficient with system resources. This is particularly beneficial for older or less powerful computers.
  • MATE Integration
    As part of the MATE desktop environment, Atril is well-integrated and maintains a consistent look and feel with other MATE applications, providing a seamless user experience.
  • Multiple Format Support
    Atril supports a variety of document formats, including PDF, PostScript, DjVu, DVI, and XPS, making it a versatile tool for viewing different kinds of documents.
  • Open Source
    Atril is open-source software, allowing users to freely inspect, modify, and distribute the software. This fosters transparency and community contributions.

Possible disadvantages of Atril

  • Limited Features
    Compared to some other document viewers, Atril may have fewer advanced features, such as extensive annotation tools or advanced search capabilities.
  • MATE Dependency
    While Atril can be used outside the MATE desktop environment, it is specifically designed for MATE, meaning it may not integrate as well with other desktop environments.
  • Occasional Lag with Large Files
    Users may experience some performance lag when opening very large documents, which can affect usability.
  • Development Pace
    Being part of a community-driven project, Atril's development and updates can be slower compared to commercial software with dedicated development teams.

Matplotlib features and specs

  • Versatility
    Matplotlib can generate a wide variety of plots, ranging from simple line plots to complex 3D plots. This versatility makes it a go-to library for many scientific and technical visualizations.
  • Customization
    It offers extensive customization options for virtually every element of a plot, including colors, labels, line styles, and more, allowing users to tailor plots to meet specific needs.
  • Integrations
    Matplotlib integrates well with other Python libraries such as NumPy, Pandas, and SciPy, making it easier to plot data directly from these sources.
  • Community and Documentation
    It has a large, active community and comprehensive documentation that includes tutorials, examples, and detailed references, which can help users solve problems and improve their plot-making skills.
  • Interactivity
    Matplotlib supports interactive plots, which can be embedded in Jupyter notebooks and GUIs, allowing for dynamic data exploration and presentation.
  • Publication-Quality
    The library is capable of producing high-quality, publication-ready graphics that meet the stringent requirements of academic journals and professional presentations.

Possible disadvantages of Matplotlib

  • Complexity
    While Matplotlib offers extensive customization, it can be complex and sometimes unintuitive for beginners, requiring a steep learning curve to master all its functionality.
  • Performance
    Rendering a large number of plots or handling very large datasets can be slow, making Matplotlib less suitable for real-time data visualization.
  • Modern Aesthetics
    Out-of-the-box plots from Matplotlib can look somewhat dated compared to those from newer plotting libraries like Seaborn or Plotly, requiring additional customization to achieve a modern look.
  • 3D Plots
    Although Matplotlib supports 3D plotting, its capabilities are relatively limited and less sophisticated compared to specialized 3D plotting libraries.
  • Size and Structure
    The package is relatively large and can be slow to import. Its extensive structure can make finding specific functions and understanding the overall architecture challenging.

Analysis of Atril

Overall verdict

  • Atril is considered a good option for users looking for a straightforward, reliable document viewer integrated with the MATE desktop environment. It offers essential features required for everyday document viewing tasks and is consistently maintained as part of the MATE project.

Why this product is good

  • Atril, a document viewer that is part of the MATE desktop environment, is appreciated for its simplicity and efficiency. It supports a wide range of document formats including PDF, PostScript, DJVU, and many others. Atril is lightweight, making it an excellent choice for users who prefer minimal resource consumption without sacrificing functionality.

Recommended for

    Atril is recommended for users who utilize the MATE desktop environment or those who need a fast and efficient document viewer that does not hog system resources. It's especially suitable for Linux users who appreciate the traditional desktop experience provided by MATE.

Analysis of Matplotlib

Overall verdict

  • Yes, Matplotlib is a good library for data visualization, particularly for users who require a versatile and powerful plotting solution in Python.

Why this product is good

  • Matplotlib is highly regarded due to its extensive customization options, versatility in creating a wide range of static, animated, and interactive plots, and its large user community and support. It integrates well with other scientific libraries in Python, making it a staple for data visualization. The library is also open-source and frequently updated, ensuring it remains a reliable choice for users.

Recommended for

  • Data scientists and analysts needing to create detailed, customized visual representations of their data.
  • Researchers and engineers looking for a comprehensive plotting library that supports scientific and engineering formats.
  • Python developers who require integration with other scientific computing libraries like NumPy and Pandas.

Atril videos

+ Add

Me comprรฉ un atril! - PDP CS800 #Review

More videos:

  • Review - REVIEW / Atril de platillo HC33BW con boom Tama
  • Review - Review: Wostoo Teclado Electrรณnico Piano 61 Teclas con Atril y Microfono

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

0-100% (relative to Atril and Matplotlib)

Atril

Matplotlib

PDF Editor
100 100%
PDF Editor
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Office & Productivity
100 100%
Office & Productivity
0% 0
Technical Computing
0 0%
Technical Computing
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 Atril and Matplotlib

Atril Reviews

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Matplotlib Reviews

25 Python Frameworks to Master
Matplotlib is a widely used tool for data visualization in Python. It provides an object-oriented API for embedding plots into applications.
Source: kinsta.com
5 Best Python Libraries For Data Visualization in 2023
You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.
Source: www.technotification.com
15 data science tools to consider using in 2021
Matplotlib is an open source Python plotting library that's used to read, import and visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers and various GUI toolkits.
Source: searchbusinessanalytics.techtarget.com
Top Python Libraries For Image Processing In 2021
Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. It is effective to get information out of an image. It doesnโ€™t support all file formats.
Source: www.analyticsvidhya.com
Top 8 Python Libraries for Data Visualization
Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Matplotlib should be more popular than Atril. It has been mentiond 114 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.

Atril mentions (23)

  • KDE celebrates the 29th birthday and kicks off the yearly fundraiser
    MATE was forked around the time GNOME 3 was released and is still going. https://mate-desktop.org Some people consider Cinnamon to be a GNOME 2 spiritual successor while still using a lot of GNOME 3 stuff under the hood. https://projects.linuxmint.com/cinnamon/. - Source: Hacker News / 9 months ago
  • SerenityOS is a love letter to '90s user interfaces
    The closest I know of is Blue95. I have only run the live environment but it worked pretty well and was impressive. "Blue95 is a modern and lightweight desktop experience that is reminiscent of a bygone era of computing. Based on Fedora Atomic Xfce with the Chicago95 theme." https://github.com/winblues/blue95 And if you like Gnome 2.x, there's MATE: https://mate-desktop.org/. - Source: Hacker News / about 1 year ago
  • Systemd Rolling Out "run0" As sudo Alternative
    I don't know if you are DE shopping, but I've been very happy for the past few years with the MATE Desktop Environment, which "...is the continuation of GNOME 2. It provides an intuitive and attractive desktop environment using traditional metaphors for Linux and other Unix-like operating systems." https://mate-desktop.org/ Among a great number of things I really like, I will mention that Caja, the MATE version of... - Source: Hacker News / about 2 years ago
  • Lobotomizing Gnome
    I agree that there is a balance between customization and "cleanness" in design and implementation. However, I think the GNOME 3 and 4 designers went too far and alienated many users: https://www.zdnet.com/article/linus-torvalds-finds-gnome-3-4-to-be-a-total-user-experience-design-failure/ https://medium.com/@fulalas/gnome-42-the-nonsense-continues-7d96c3287f7... - Source: Hacker News / about 3 years ago
  • I Still Use Windows 95 (archived, 2008)
    > Is there a WM out there that can do the basic quality-of-life functions of today's DEs? I'd love a simple, opinionated WM that takes the features we know are useful today (workspaces, expo mode, sensible file manager layouts, system trays) and gives them a color-adjustable window theme inspired by 90's aesthetics, with minimal compositing that can run fast on hardware as minimal as a prototype RISC-V board. Or... - Source: Hacker News / about 3 years ago
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Matplotlib mentions (114)

  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ€” the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 4 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes itโ€™s easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 7 months ago
  • libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators
    We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
  • Building an AI Scoring Agent: Step-By-Step
    NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 10 months ago
View more

What are some alternatives?

When comparing Atril and Matplotlib, you can also consider the following products

Evince - Evince is a document viewer for multiple document formats: PDF, Postscript, djvu, tiff, dvi, XPS...

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

PDF Reader Pro - PDF Reader Pro is an all-in-one PDF office supporting to Read, Annotate, Edit, OCR, Convert, Create & Fill Form, Sign PDFs, TTS on Mac, iOS, Android, and Windows.

NumPy - NumPy is the fundamental package for scientific computing with Python

ApowerPDF - ApowerPDF is a versatile PDF editor which also features as PDF converter, viewer, creator and more. It provides a perfect solution for all users.

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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