Scientific Visualization

A talk I gave in the Department of Scientific Computing at Florida State University covering scientific visualization.

Transcript

1. Scientific Visualization By Eitan Lees
2. Scientific Visualization By Eitan Lees * * Mostly in python
3. Scientific Visualization By Eitan Lees * * Mostly in python Data
4. Scientific Visualization By Eitan Lees * * Mostly in python Data Plots
5. By Eitan Lees Data Plots
6. Scientific Visualization By Eitan Lees
8. Acknowledgements - Sachin Shanbhag for letting me explore - Nathan Crock for putting it all together - Much of today’s talk comes from Jake Vanderplas’ PyCon 2017 talk - Brian Granger and the Jupyter project
9. Show and Tell
10. So you want to make a plot?
11. - Visit - Mongo - Vesta - Gnuplot - TikZ - Matlab - Matplotlib - Adobe Illustrator
13. Government Solutions
16. Advantages: - All in one platform - Developed with scientific application in mind - Very popular for physical modeling Weakness: - Too much for simple plots - GUI forward - Stability
17. Commercial Solutions
18. Data Visualization
20. Python Visualization Landscape
22. Strengths: - Designed like MatLab: switching was easy
23. Strengths: - Designed like MatLab: switching was easy - Many rendering backends
24. Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort)
25. Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort) - Well-tested, standard tool for over a decade
27. Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort) - Well-tested, standard tool for over a decade Weaknesses: - API is imperative & often overly verbose - Poor support for web/interactive graphics - Often slow for large & complicated data
28. Objective: Improve on the weaknesses of matplotlib (without sacrificing the strengths!)
29. Building on Matplotlib Common Idea: Keep matplotlib as a versatile well tested backend, and provide a new domain specific API.
30. Key Features: - Provides the DataFrame object - Also provides a simple API for plotting
31. Key Features: - Provides the DataFrame object - Also provides a simple API for plotting - Recently more sophisticated statistical visualization tools have been added
32. Key Features: - Like pandas, wraps matplotlib - Nice set of color palettes & plot styles - Focus on statistical visualization & modeling
36. But what about the internet?!?
39. Common Idea: build a new API that produces a plot serialization (often JSON) that can be displayed in the browser (often in Jupyter notebooks)
40. Bokeh
42. Bokeh Advantages: - Web view/interactivity - Handles large data and/or streaming datasets - Geographical visualizations - Fully open source Weakness: - Plotly has some paid features - Limited output formats - Smaller (but growing) community
45. The elephant in the room
47. D3 is everywhere
48. But working with D3 can be challenging ...
49. “Simple” Barchart
50. D3 is a Javascript package that streamlines the manipulation of objects on a webpage
51. Vega is a detailed declarative specification for visualizations, built on D3
52. Vega-Lite is a simpler declarative specification aimed at statistical visualization
53. Altair is a Python API for creating Vega-Lite specifications
55. Bridging the gap
57. - Aggregates data and sends pixels - Can handle interactive visualization of billions of rows - Datasets themselves stored in objects that automatically produce intelligent visualizations
58. Don’t care about the internet?
59. Want more power?
63. The Python Visualization Landscape
64. Toward Declarative Visualization
65. What do you mean by declarative visualization?
66. Example: Statistical Data Tidy data: i.e. rows are samples, columns are features
67. Tidy data: i.e. rows are samples, columns are features “ I want to scatter petal length vs. sepal length, and color by species” Example: Statistical Data
70. Problem: We’re mixing the what with the how
71. Toward a well-motivated Declarative Visualization Imperative - Specify How something should be done. - Specification & Execution intertwined. - “Put a red circle here and a blue circle here” Declarative - Specify What should be done. - Separates Specification from Execution - “Map <x> to a position, and <y> to a color” Declarative visualizations lets you think about the data and relationships, rather than incidental details
72. Toward a well-motivated Declarative Visualization Imperative - Specify How something should be done. - Specification & Execution intertwined. - “Put a red circle here and a blue circle here” Declarative - Specify What should be done. - Separates Specification from Execution - “Map <x> to a position, and <y> to a color” Declarative visualizations lets you think about the data and relationships, rather than incidental details
73. Based on the Vega and Vega-Lite grammars https://altair-viz.github.io/ Altair Declarative visualization in Python
74. Altair for Statistical Visualization
75. Encodings are Flexible:
76. Altair is Interactive
77. ...
78. ... Altair Tutorial THIS Friday in THIS room from 3:30-4:30 pm