Matplotlib: A 2D Graphics Environment

By John D. Hunter. 2007

24,959 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Graphics and Computer-Aided Design, Numerical Methods & Scientific Computing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yoshua Bengio
2. Mohit Bansal
3. Sergey Levine
4. Percy Liang
5. Quoc V. Le
6. Ruslan Salakhutdinov
7. Jianfeng Gao
8. Kyunghyun Cho

In Computer Graphics and Computer-Aided Design

1. C. Stringer
2. Marius Pachitariu
3. Matthew J. Turk
4. L. Hernquist
5. Matthias Bethge
6. Tom Abel
7. S. Furlanetto
8. A. Mesinger

In Numerical Methods & Scientific Computing

1. Christoph Weniger
2. Jonathan J. Helmus
3. C. Jaroniec
4. Valeria C. F. Barbosa
5. Leonardo Uieda
6. J. Vanderplas
7. Carlos Henrique Grohmann
8. Colin Raffel

Citations in the corpus

• Theano: new features and speed improvements (2012, methodology): “ules for tensor manipulations and plotting, while Theano benefits from various external Python libraries to perform those tasks (notably SciPy (Jones et al., 2001), NumPy (Oliphant, 2007), matplotlib (Hunter, 2007), IPython (Perez and Granger, 2007)).´ 4.1.2 Main differences Some of Torch7’s strength”
• Array programming with NumPy (2020, background): “[14] J.”
• Understanding the Origins of Bias in Word Embeddings (2018, neutral): “Figures were generated using Matplotlib [10].”
• Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, methodology): “Embedded graphs are computed with the free matplotlib Python library (Hunter, 2007).”
• Data Poisoning Attacks against Autoregressive Models (2016, neutral): “4.3 (Hunter 2007).”
• Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, positive): “All computational analysis and scripting was conducted in Python 3.7 [65], using the following libraries: Pandas dataframes [36] for data parsing and transformation; SciPy [28] and NumPy [64] for quantitative computations; and Matplotlib [25] and Seaborn [67] for visualization.”
• SymPy: Symbolic computing in Python (2017, methodology): “Plotting Hooks for visualizing expressions via matplotlib [20] or as text drawings when lacking a graphical back-end.”
• Making Efficient Use of Demonstrations to Solve Hard Exploration Problems (2019, background): “, 2010), Matplotlib (Hunter, 2007) and Seaborn (Waskom et al.”
• SCANPY: large-scale single-cell gene expression data analysis (2018, background): “S CANPY ’s core relies on N UMPY [33], S CIPY [34], M AT - PLOTLIB [35], P ANDAS [36], and H 5 PY [37].”
• Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning (2018, methodology): “All the attention masks we display were generated using a perceptually-uniform color map [11].”
• Towards mental time travel: a hierarchical memory for reinforcement learning agents (2021, background): “All figures were made with seaborn [59] and matplotlib [22].”
• Exploring Network Structure, Dynamics, and Function using NetworkX (2008, methodology): “For visualizing and drawing, NetworkX contains interfaces to the Graphviz network layout tools [EGK04], Matplotlib (2d) [Hun07] and UbiGraph (3d) [Vel07].”